This summarizes a study that used sequential masking classification of multi-temporal Landsat images from May, July, and August 2000 to map crops in Karacabey, Turkey on a field-by-field basis. Individual pixel classifications were first performed on each image and integrated with digitized field boundaries. Crops above an accuracy threshold in each image were then excluded in sequential masking classifications of the other dates to improve discrimination between spectrally similar crops. This increased overall accuracies of the July and August classifications by over 10% compared to classifying them alone.
Spatial analysis for the assessment of the environmental changes in the lands...Universität Salzburg
Presented research is focused on the spatial analysis aimed at the assessment of the environmental changes in the landscapes of Izmir surroundings, Turkey. Methods include Landsat TM images classification using Erdas Imagine, clustering segmentation and classification, verification via the Google Earth and GIS Mapping. Tme span is 13-years (1987-2000). Images were taken from the Global Land Cover Facility (GLCF) Earth Science Data Interface. The selected area of Izmir has the most diverse landscape structure and high heterogeneity of the land cover types. Accuracy results computed. Kappa statistics for the image 2000: 0.7843, for the image 1987: 0.7923. The classification of the image 1987: accuracy 81.25%, 2000: 80,47%. The results indicate changes in land cover types affected by human activities, i.e. increased agricultural areas. Results include following findings. 1987: croplands (wheat) covered 71% of the today’s area (2000): 2382 vs. 3345 ha. Increase in barley cropland areas is noticeable as well: 1149 ha in 1987 vs. 4423 ha in 2000. Sparsely vegetated areas now also occupy more areas : 5914 ha in 2000 against 859 ha in 1987. Natural vegetation, decreased, which can be explained by the expansion of the agricultural lands. 1987: coppice areas covered 5500 ha while later on there are only 700 ha in this land type.
Spatial analysis for the assessment of the environmental changes in the lands...Universität Salzburg
Presented research is focused on the spatial analysis aimed at the assessment of the environmental changes in the landscapes of Izmir surroundings, Turkey. Methods include Landsat TM images classification using Erdas Imagine, clustering segmentation and classification, verification via the Google Earth and GIS Mapping. Tme span is 13-years (1987-2000). Images were taken from the Global Land Cover Facility (GLCF) Earth Science Data Interface. The selected area of Izmir has the most diverse landscape structure and high heterogeneity of the land cover types. Accuracy results computed. Kappa statistics for the image 2000: 0.7843, for the image 1987: 0.7923. The classification of the image 1987: accuracy 81.25%, 2000: 80,47%. The results indicate changes in land cover types affected by human activities, i.e. increased agricultural areas. Results include following findings. 1987: croplands (wheat) covered 71% of the today’s area (2000): 2382 vs. 3345 ha. Increase in barley cropland areas is noticeable as well: 1149 ha in 1987 vs. 4423 ha in 2000. Sparsely vegetated areas now also occupy more areas : 5914 ha in 2000 against 859 ha in 1987. Natural vegetation, decreased, which can be explained by the expansion of the agricultural lands. 1987: coppice areas covered 5500 ha while later on there are only 700 ha in this land type.
Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Sat...TELKOMNIKA JOURNAL
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation
seasonal mapping both on forest and agricultural site. In order to provide a long -terms of vegetation
characteristic maps, a wide time-series images analysis is needed which require high-performance
computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in
Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of
paddy field vegetation information. This research is aimed to develop a method to produce the optimized
solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The
time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused
by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both
CPU and GPU to find the efficient and the robust result. The developed method has been tested and
implemented using the sample case of paddy fields in Java Island. The best system which can
accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are
ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.
Multiple Crop Classification Using Various Support Vector Machine Kernel Func...IJERA Editor
This study was carried out with techniques of Remote Sensing (RS) based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF). The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA) to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.
Assessment, Monitoring and Managing SOC for Climate Change Mitigation and Ada...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. K. Suresh Chaudhari, from ICAR - India, in FAO Hq, Rome
My special talk on 'GIS & Remote Sensing-Introduction to the Primer’ is a part of the 'Learn from the Leaders- 2' webinar series organized by IEEE SIGHT, Bombay section on May 25th, 2021
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Remote sensing is the acquiring of information from a distance and also the art and science of making measurements of the earth using airborne- or satellite-based sensors, is used in GIS. GIS is a collection of computer-based tools for organizing information from a variety of data sources to map and examine changes on Earth. Global Positioning System or GPS is a space-based radio navigation system that helps pinpoint a three-dimensional position to about a meter of accuracy (for example latitude, longitude and altitude) and provide nano-second precise time anywhere on Earth.
Change detection analysis of Cropland using Geospatial technique -A case Stud...IJEAB
Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.
Topographic Information System as a Tool for Environmental Management, a Case...iosrjce
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Object-Oriented Image Processing Of An High Resolution Satellite Imagery With...CSCJournals
The management of urban areas by urban planners relies on detailed and updated knowledge of their nature and distribution. Manual photo-interpretation of aerial photographs is efficient, but is time consuming. Image segmentation and object-oriented classifications provide a tool to automatically delineate and label urban areas. Here single pixels are not classified but objects created in multi-resolution segmentation process, which allows use of, spectral responses but also texture, context and information from other object layers. This paper presents a methodology allowing to derive meaningful area-wide spatial information for city development and management from high resolution imagery. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology
P.S.Jagadeesh Kumar, Tracy Lin Huan, Xianpei Li, Yanmin Yuan. (2018) ‘Panchromatic and Multispectral Remote Sensing Image Fusion using Particle Swarm Optimization of Convolutional Neural Network for Effective Comparison of Bucolic and Farming Region’, Earth Science and Remote Sensing Applications, Series of Remote Sensing/Photogrammetry, Vol. 43, pp.1-31, Springer.
Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Sat...TELKOMNIKA JOURNAL
Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation
seasonal mapping both on forest and agricultural site. In order to provide a long -terms of vegetation
characteristic maps, a wide time-series images analysis is needed which require high-performance
computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in
Indonesia, that analysis has to be employed gradually and endlessly to provide the latest condition of
paddy field vegetation information. This research is aimed to develop a method to produce the optimized
solution in classifying vegetation of paddy fields that diverse both spatial and temporal characteristics. The
time-series EVI data from MODIS have been filtered using wavelet transform to reduce noise that caused
by cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in both
CPU and GPU to find the efficient and the robust result. The developed method has been tested and
implemented using the sample case of paddy fields in Java Island. The best system which can
accommodate of the extend-ability, affordability, redundancy, energy-saving, maintainability indicators are
ARM-based processor (Raspberry Pi), with the highest speed up of 8 and the efficiency of 60%.
Multiple Crop Classification Using Various Support Vector Machine Kernel Func...IJERA Editor
This study was carried out with techniques of Remote Sensing (RS) based crop discrimination and area estimation with single date approach. Several kernel functions are employed and compared in this study for mapping the input space with including linear, sigmoid, and polynomial and Radial Basis Function (RBF). The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISS-III data was used for further classification on training set for supervised classification. ENVI 4.4 is used for image analysis and interpretation. The experimental tests show that system is achieved 94.82% using SVM with kernel functions including Polynomial kernel function compared with Radial Basis Function, Sigmoid and linear kernel. The Overall Accuracy (OA) to up to 5.17% in comparison to using sigmoid kernel function, and up to 3.45% in comparison to a 3rd degree polynomial kernel function and RBF with 200 as a penalty parameter.
Assessment, Monitoring and Managing SOC for Climate Change Mitigation and Ada...ExternalEvents
This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. K. Suresh Chaudhari, from ICAR - India, in FAO Hq, Rome
My special talk on 'GIS & Remote Sensing-Introduction to the Primer’ is a part of the 'Learn from the Leaders- 2' webinar series organized by IEEE SIGHT, Bombay section on May 25th, 2021
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Remote sensing is the acquiring of information from a distance and also the art and science of making measurements of the earth using airborne- or satellite-based sensors, is used in GIS. GIS is a collection of computer-based tools for organizing information from a variety of data sources to map and examine changes on Earth. Global Positioning System or GPS is a space-based radio navigation system that helps pinpoint a three-dimensional position to about a meter of accuracy (for example latitude, longitude and altitude) and provide nano-second precise time anywhere on Earth.
Change detection analysis of Cropland using Geospatial technique -A case Stud...IJEAB
Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.
Topographic Information System as a Tool for Environmental Management, a Case...iosrjce
IOSR Journal of Environmental Science, Toxicology and Food Technology (IOSR-JESTFT) multidisciplinary peer-reviewed Journal with reputable academics and experts as board member. IOSR-JESTFT is designed for the prompt publication of peer-reviewed articles in all areas of subject. The journal articles will be accessed freely online.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Object-Oriented Image Processing Of An High Resolution Satellite Imagery With...CSCJournals
The management of urban areas by urban planners relies on detailed and updated knowledge of their nature and distribution. Manual photo-interpretation of aerial photographs is efficient, but is time consuming. Image segmentation and object-oriented classifications provide a tool to automatically delineate and label urban areas. Here single pixels are not classified but objects created in multi-resolution segmentation process, which allows use of, spectral responses but also texture, context and information from other object layers. This paper presents a methodology allowing to derive meaningful area-wide spatial information for city development and management from high resolution imagery. Finally, the urban land cover classification is used to compute a spatial distribution of built-up densities within the city and to map homogeneous zones or structures of urban morphology
P.S.Jagadeesh Kumar, Tracy Lin Huan, Xianpei Li, Yanmin Yuan. (2018) ‘Panchromatic and Multispectral Remote Sensing Image Fusion using Particle Swarm Optimization of Convolutional Neural Network for Effective Comparison of Bucolic and Farming Region’, Earth Science and Remote Sensing Applications, Series of Remote Sensing/Photogrammetry, Vol. 43, pp.1-31, Springer.
Object Classification of Satellite Images Using Cluster Repulsion Based Kerne...IOSR Journals
Abstract: We investigated the Classification of satellite images and multispectral remote sensing data .we
focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for
classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area
and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian
filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using
proposed Cluster repulsion based kernel Fuzzy C- Means (FCM) and (c) classification using one-to-many SVM
classifier. The goal of this research is to provide the efficiency in classification of satellite images using the
object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the
proposed work is compared to FCM based classification. The results showed that the proposed technique has
achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle
classification respectively.
Keywords:-Satellite image, FCM Clustering, Classification, SVM classifier.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance of RGB and L Base Supervised Classification Technique Using Multi...IJERA Editor
In the present growth of sensor technology is to improve the new chance and applications in GIS. This enhances the technology law a new method that should not focus on real time available products, but it must automatically lead to new ones. The aim of the paper is to make a maximum use of remote sensing data and GIS techniques to access land use and land cover classification in the Kiliyar sub basin sector in palar river of northen part of Tamil Nadu.IRS P6 LISS III is merged data to perform the classification using ERDAS Imaging. The RGB and L base supervised classification was based up on a Multispectral analysis, land use and land cover information‟s (maps and existing reports), which involves advanced technology and complex data processing to find detailed imagery in the study region. Ground surface reflects more radar energy emitted by the sensor from the study region, which makes it easy to distinguish between the water body, hilly, agriculture, settlement and wetland.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Analysis of Cultivated Land in Maonan District, Maoming Based on RS and GISAI Publications
Food is the most basic human survival need, and cultivated land is the basis of food production. Thereby, monitoring and analysis of cultivated land is the premise and foundation of sustainable agricultural development. In this paper, Maonan District of Maoming City was taken as the research area. The object-oriented classification and cultivated land extraction were carried out by Landsat-8 remote sensing (RS) images, and then a confusion matrix and kappa coefficient were used to evaluate the accuracy of the cultivated land extraction results. The results show that the cultivated land in this area is decreasing. The extraction results of the confusion matrix and Kappa coefficient evaluation were accurate, and the Kappa coefficients of the three different periods were all higher than 0.6. Moreover, the main reasons for the decrease of cultivated land in the study area are population growth, socio-economic development, and the adjustment and change of agricultural structure.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
37
1. FIELD-BASED CROP MAPPING THROUGH SEQUENTIAL MASKING
CLASSIFICATION OF MULTI-TEMPORAL LANDSAT-7 ETM+ IMAGES IN
KARACABEY, TURKEY
M. Turker *, M. Arikan
Middle East Technical University, Graduate School of Natural and Applied Sciences
Geodetic and Geographic Information Technologies, 06531 Ankara, Turkey - mturker@metu.edu.tr
Commission VII, WG VII/2
KEYWORDS: Landsat, Agriculture, Crop, Classification, Multitemporal, Integration, Object
ABSTRACT:
This study presents a field-based crop mapping through sequential masking classification of multi-temporal Landsat-7 ETM+ images
acquired in May, July, and August 2000 in Karacabey, Turkey. First, the classification of each image date was carried out on a
standard per pixel basis. The results of the per pixel classification were integrated with digital agricultural field boundaries and for
each field, a crop type was determined based on the modal crop class calculated within the field. The classification accuracy was
computed by comparing the reference data, field-by-field, to each classified image. The individual crop accuracies were examined on
each classified data to determine those crops whose accuracy exceeds a preset threshold level. Then, the multi-temporal masking
classification of the crops was carried out in sequential steps using the three image dates, excluding after each classification the crop
properly classified. The masking technique was applied to overcome the problems caused by the spectral overlaps between some
classes. The final classified data was analyzed in a field specific manner to assign each field a crop label. An immediate update of the
database was provided by directly entering the results of the analysis into the database. The use of sequential masking procedure for
field-based crop mapping improved the overall accuracies of the classifications of the July and August images alone by more than
10%.
1. INTRODUCTION
The availability of remotely sensed images and the advances
in digital processing and analysis techniques have enabled
research scientists to have information about the type,
condition, area, and the growth of agricultural crops. Image
classification is one of the crucial techniqes in detecting the
crops from remotely sensed data. Most current automatic
classification techniques to obtain land cover maps from
digital imagery operate on a per-pixel basis in isolation from
other pertinent information. Therefore, per-pixel techniques
often yield results with limited reliability. The reliability of
image classification can be improved by including apriori
knowledge about the contextual relationships of the pixels in
the classification process. Agricultural field boundaries
integrated with remotely sensed data divide the image into
homogeneous units each of which can be analyzed seperately.
In each field, the geometry of field boundaries defines the
spatial relationships between the pixels contained within, and
enables those pixels to be processed in coherence. The
decision by the analysis is taken, for each field, based on the
coherent processing of the pixels falling within the field.
Therefore, the standard per-pixel image classification can be
replaced by a classification which operates in a field specific
manner.
Field-based approaches to the classification have been
adopted by several researchers (Catlow et al. 1984; Mason et
al. 1988; Janssen et al. 1990; Janssen et al. 1992; Aplin et al.
1999; Turker and Derenyi 2000; Aplin and Atkinson 2001).
To perform field-based classification, the vector field
boundaries must be integrated with the imagery. The
integration between the two data sets can be achived at three
stages: (i) before classification, (ii) during classification, and
(iii) after classification. Usually, field-based classification
employs the integration between raster imagery and vector
data after classification (Brisco et al. 1989; Janssen et al.
1990; Janssen et al. 1992; Aplin et al. 1999; Turker and
Derenyi 2000; Aplin and Atkinson 2001). The imagery is
classified on a per-pixel basis before integrating the classified
output with digital vector data. A per-field analysis is then
carried out to assign each field a class label based on the
analysis of the classified pixels contained within the field.
The success of a field-based approach that incorporates
vector data after a per-pixel classification depends mainly on
the success of the classification. Several studies have shown
that multi-temporal images improve the classification
accuracy by utilizing different spectral responses of the land
cover classes over a period of time according to phenological
evolution (Maracci and Aifadopoulou 1990; Conesa and
Maselli 1991; Kurosu et al. 1997; Panigrahy and Sharma
1997; Beltran et al. 2001; Lanjeri et al. 2001; Murakami et
al. 2001).
The objective of this study was field-based mapping of
summer (August) crops in Karacabey, Turkey through
sequential masking classification of Landsat7 ETM+ satellite
data. The sequential masking classification technique was
applied to improve discrimination between the crop classes.
We made an assumption that each field grows one type of
crop. Field-based classification was performed by computing
the percentages of classified pixels within each field and
assigning a class label to the field based on the majority class.
The fields were selected through a database query and the
results were directly inserted into the database.
* Corresponding author
2. 2. THE STUDY AREA AND DATA DESCRIPTION
The study area is a 18 km by 13.5 km site situated near the
town of Karacabey, Bursa in northwest of Turkey (Figure 1).
The geographic boundaries of the area are N40°07′44″ -
N40°13′43″ and E28°10′31″ - E28°20′28″. The main crops
grown in the region are tomato, corn, pepper, wheat, onion,
and sugar beet. The villages that fall within the study area
include Akhisar, Eskisaribey, Hotanli, Ismetpasa,
Kucukkaraagac, Sultaniye, Yenisaribey, and Yolagzi. In the
region, a land consolidation project was performed between
1988 and 1992. Therefore, majority of the fields have regular
shapes that affect the classification accuracy. However, a
significant number of small fields exist in the area. The size
of the fields range from 0.0074 to 48 ha. The elevation
difference across the study area is very small (within 10 m).
The Landsat7 ETM+ images (Path:180, Row:32) used in the
classification were acquired on May 15, 2000, July 2, 2000,
and August 19, 2000. All images were cloud free and of good
quality. A 600 pixels x 450 lines (multispectral) subscene
covering the study area was extracted to perform the
proposed sequential masking classification procedure.
Figure 1. The study area.
3. DATA PRE-PROCESSING
The field boundaries were digitized from the cadastral map
sheets. The maps were converted into a raster form by
scanning them. Each raster map was registered to Gauss-
Kruger (Zone-5) projection and datum ED50 using six grid
intersections. The registration was based on first-degree
polynomial and nearest neighbor resampling techniques. The
registration accuracies were between ± 1.14 m and ± 3.59 m.
Next, the field boundaries were manually delineated through
on screen digitization and stored as vector polygons in the
database. Each polygon was assigned an identification
number and the crop types were recorded as attributes for
those fields from which reference information was collected
during site visit. There were a total of 2977 fields.
The multispectral bands (1-to-5 and 7) and the panchromatic
band were merged for each image date. The fused image
retains the spatial resolution of the panchromatic band, yet
provides the spectral properties of multispectral bands.
Furthermore, the field-based classification of a higher spatial
resolution image would be adventageous for small fields
since the number of pixels falling within the fields will
increase. The fused images were geometrically corrected to
Universal Transverse Mercator (UTM)-zone 35 projection
and the European Datum 1950 (ED50). The geometric
correction was based on second-degree polynomial and
nearest neighbor resampling techniques. The root mean
square error (RMSE) values were computed as ± 0.52 pixels
for the May image, ± 0.67 pixels for the July image, and ±
0.59 pixels for the August image.
We displayed the merged August image on the screen with
the digitized field boundaries overlaid, and those fields (275
in total) that contain multiple crops were identified through
visual inspection. The sub-boundaries within the fixed
geometry of these fields were then delineated through on
screen digitization and each sub-field was assigned an
identification number. There were 424 new sub-fields. With
these new sub-fields the total number of fields increased to
3401.
4. THE METHODOLOGY
A total of 1083 fields were visited on the ground. For each
field, crop type was collected as essential information and the
database was populated with the new data. For each image
date, the training samples were selected from all crops found
in the study area. The field boundaries were displayed on the
screen in superimposition with the raster imagery. For each
class, those fields from which reference information was
collected during site visit were selected through a database
query. Thereby, the boundary pixels were avoided. The
following classes were defined for the May image: bare soil,
wheat, clover, pasture, rice, pea, and onion. The July classes
include corn, residue, tomato, sugar beet, clover, pasture,
pepper, watermelon, bare soil, rice, cauliflower, and onion.
The classes defined for the August image are the same as the
July classes except for onion, which does not exist in August.
For each class, a group of representative pixels were
delineated within designated fields. Approximately 10% of
the ground-visited fields were used in training, while the
other fields were set aside to be used as check fields to
perform accuracy assessement.
A per-pixel supervised classification of the images was
performed using the maximum likelihood method. For each
image date, the classification was carried out on bands 1, 2, 3,
4, 5, and 7. In addition, Principal Component Analysis (PCA)
was carried out to obtain new channels. In the present case
the first 4 components of the May, July, and August images
provided 99.77%, 99.60%, and 99.76% respectively of the
total variance of the original data sets. Therefore, the first 4
PCs were used in the classification.
Upon completing the per-pixel classification, the integrated
analysis of the classified images and the vector field data was
carried out. To eliminate the effect of the boundary pixels on
the classification, a narrow corridor was generated along the
boundary inside each field. Field-based analysis was
performed by computing the class percentages within inside
corridor boundaries of each field and applying the entire field
the label of the modal class. For each field, the class
percentages and the final class label were automatically
inserted into the database. To compute classification
accuracy, the reference data were compared, field-by-field, to
each of the six classified images. The error matrix was used
to measure the accuracy of each classified output. The
accuracies of individual classes and the overall accuracy of
all bands and the first 4 PCs classifications are given in tables
1 and 2.
Ankara
Istanbul
Study
Area
Ï
N
• Bursa
3. May July August
PA
UA
PA
UA
PA
(%)
(%)
(%)
(%)
(%)
UA
(%)
Bs 94.2 97.9 Cr 56.1 76.1 Cr 76.0 87.4
Wt 86.8 99.4 Rs 93.1 92.6 Rs 70.2 98.9
Cl 50.0 100 Tm 71.9 65.8 Tm 85.4 72.3
Pa 100 64.7 Sb 78.3 95.4 Sb 60.4 95.3
Rc 50.0 62.5 Cl 50.0 100 Cl 50.0 100
Pe 60.0 81.8 Pa 66.7 83.3 Pa 100 40.0
On 60.0 10.0 Pp 16.4 18.0 Pp 50.9 32.9
Overall: 88.9 Wm 7.7 25.0 Wm 57.1 80.0
Bs 80.0 9.8 Bs 22.4 42.9
Rc 80.0 100 Rc 90.0 75.0
Cw 34.3 48.0 Cw 57.1 27.0
On 50.0 7.0
Overall: 66.8
Overall: 70.8
Table1. Producer’s and user’s accuracies of individual classes
for the classification of all bands.
Bs: Bare Soil; Wt: Wheat; Cl: Clover; Pa: Pasture; Rc: Rice;
Pe: Pea; On: Onion; Cr: Corn; Rs: Residue; Tm: Tomato;
Sb: Sugar Beet; Pp: Pepper; Wm: Watermelon; Cw:
Cauliflower; PA: Producer’s Accuracy; UA: User’s Accuracy
May July August
PA
UA
PA
UA
PA
(%)
(%)
(%)
(%)
(%)
UA
(%)
Bs 92.8 98.3 Cr 43.9 67.7 Cr 76.4 87.4
Wt 85.8 100 Rs 92.0 93.0 Rs 67.2 98.9
Cl 100 100 Tm 66.1 61.3 Tm 83.7 70.7
Pa 100 61.1 Sb 80.2 95.5 Sb 54.5 96.5
Rc 60.0 54.5 Cl 50.0 100 Cl 50.0 50.0
Pe 62.2 84.8 Pa 60.0 69.2 Pa 100 37.8
On 70.0 10.6 Pp 14.5 13.6 Pp 49.1 31.3
Wm 7.7 10.0 Wm 64.3 69.2
Overall: 88.4
Bs 40.0 3.8 Bs 23.9 37.2
Rc 70.0 50.0 Rc 80.0 80.0
Cw 28.6 34.5 Cw 51.4 25.7
On 50.0 8.5
Overall: 61.2
Overall: 69.2
Table 2. Producer’s and user’s accuracies of individual
classes for the classification of the first 4 PCs.
Next, the sequential masking classification was performed.
To design the classification procedure, we analyzed the
producer’s and user’s accuracies of individual crops and
determined those classes with high accuracy. The thresholds
of 80% and 90% were defined for producer’s and user’s
accuracies respectively. The threshold for the user’s accuracy
was kept higher for avoiding wrong masking of the fields
belonging to other classess due to commission error. The
accuracies of residue and rice were higher than the thresholds
on the all bands classification of the July image. The
producer’s and user’s accuracies of residue were 93.1% and
92.6% respectively. Rice had a user’s accuracy of 100% and
a producer’s accuracy of 80%. The other classes that meet the
preset accuracy criteria are clover and sugar beet. While the
producer’s and user’s accuracies of sugar beet were 80.2%
and 95.5% respectively on the classification of the first 4 PCs
of the July image, clover had the highest producer’s and
user’s accuracies of 100% on the classification of the first 4
PCs of the May image. The considerably high classification
accuracies of residue, rice, clover, and sugar beet can be
attributed to their phenological evolution. Therefore, a
sequential-masking classification seems to be more suitable.
It allows us to perform step-by-step classification of the
classes using the multi-date images, excluding after each
classification the class properly classified.
The main steps of the proposed sequential classification
procedure is illustrated in figure 2. We should indicate that
the areas other than agricultural fields, such as roads,
channels, and villages were not included in the classification
and were eliminated by masking them out. The classification
procedure was carried out as follows: First, clover was
masked out on the classification of the first 4 PCs of the May
image. Those fields classified as clover were excluded prior
to further classification and the class training corresponding
to clover was taken out. After this operation, the reduced
class tranings were used to perform further classification.
Next, those fields labeled as residue on the classification of
the all bands of the July image were masked out. The reduced
class training-set will now contain all crops except clover and
residue. Further classification will now be performed over
unmasked fields only. After that, those fields corresponding
to rice on the classification of the July image were masked
before proceeding the classification procedure. The last class
to be masked was sugar beet. Finally, the remaining crops
(corn, tomato, pasture, pepper, watermelon, bare soil, and
cauliflower) were classified using all the bands of the August
image through their training statistics only.
Classification of the first 4
PCs of the May Image
Classified Output
Figure 2. The sequential masking classification
procedure.
Clover
Mask
Clover
All bands classification
of the July Image
Residue and Rice
Mask Residue
and Rice
Classification of the
first 4 PCs of the July
Sugar Beet
Mask Sugar
Beet
All bands classification
of the August Image
Field-Based
Analysis
4. After completing the masking classification procedure, the
classified outputs were combined to make a single classified
image. A field-based analysis was then performed on the
final classified output by computing the class percentages
within each field and applying the entire field the label of the
modal class. The result of the multi-temporal masking
classification is illustrated in figure 3. The validation of the
masking classification was carried out by comparing the
reference data, field-by-field, to the classified image. The
individual class accuracies and the overall accuracy are
illustrated in an error matrix in table 3.
Adobe Systems
Figure 3. The result of sequential masking classification.
Reference
Cr Rs Tm Sb Cl Pa Pp Wm Bs Rc Cw
Cr 181 0 10 1 0 0 14 2 0 1 1 210
Rs 0 162 0 0 0 0 0 0 0 0 0 162
Tm 33 5 252 15 0 0 11 2 3 0 5 326
Sb 0 0 0 84 0 0 0 0 0 0 0 84
Cl 0 0 0 0 2 0 0 0 0 0 0 2
Pa 0 4 1 0 0 14 0 0 0 0 2 21
Pp 18 0 21 4 0 0 28 1 0 0 1 73
Wm 0 0 1 1 0 0 0 9 0 1 0 12
Bs 2 2 3 0 0 0 0 0 4 0 6 17
Rc 0 0 0 0 0 0 0 0 0 8 0 8
Cw 0 1 3 0 0 0 0 0 0 0 17 21
234 174 291 105 2 14 53 14 7 10 32 936
PA 77.4 93.1 86.6 80.0 100 100 52.8 64.3 57.1 80.0 53.1
UA 86.2 100 77.3 100 100 66.7 38.4 75.0 23.5 100 81.0
Overall: 81.3 %
Table 3. Error matrix for the sequential masking
classification.
5. RESULTS AND DISCUSSION
The producer’s and user’s accuracies and the overall
accuracy for the classification of all bands of the May, July,
and August images are summarized in table 1. The overall
accuracy of the May image (88.9%) was the highest. The
overall accuracies for the July and August images were
68.8% and 70.8% respectively. On May image, bare soil had
a producer’s accuracy of 94.2% and a user’s accuracy of
97.9%. The producer’s and user’s accuracies of wheat were
found to be 86.8% and 99.4% respectively. Among the
remaining classes, onion had the lowest user’s accuracy. Of
the two clover fields, one was omitted. All pasture fields
were correctly classified. However, some confusion is
evident between pea and pasture. Both the producer’s
accuracy (50%) and the user’s accuracy (62.5%) of rice were
below the overall accuracy. The user’s and producer’s
accuracies of pea were 81.8% and 60% respectively. Some
confusion is evident between the pea class and the onion and
pasture classes.
The overall accuracies of the July image (66.8%) and the
August image (70.8%) were lower than the May image. A
large number of fields (446) that were classified as bare soil
on May image contain crops at their active growth phase on
July and August images. Tomato, pepper, sugar beet, and
corn present an important vegetative development in the
June-August period. On July image, residue had the highest
producer’s accuracy of 93.1% and a very high user’s
accuracy of 92.6%. Both rice and sugar beet also exhibit
significantly high accuracies. Pepper, uncultivated land,
onion, and watermelon exhibit a significant amount of errors
of commission. The producer’s and user’s accuracies of
tomato and pasture were above the overall accuracy. The
user’s accuracy of clover was computed to be 100% but the
producer’s accuracy (50%) was significantly low. Similarly,
corn exhibits a relatively high user’s accuracy (76.1%) and a
rather low producer’s accuracy (56.1%). The classification of
the August image indicates that the producer’s and user’s
accuracies of corn, tomato, pepper and watermelon increased.
The user’s accuracy of residue and uncultivated land
improved but their producer’s accuracies decreased. The
reverse trend is observed for pasture, rice, and cauliflower
which exhibit higher producer’s accuracy and lower user’s
accuracy in the August image classification than the July
image classification. The August image classification
revealed no difference in the accuracies of clover.
The results of the first 4 PCs classification (Table 2) reveal
that the overall accuracy of the May image (88.4%) is the
highest. Similar to all bands classification (Table 1) the
overall accuracies of both the July and August images are
remarkably lower than that of May image. When we compare
the overall accuracies of the PCs classification to that of all
bands classification it is evident that the PCs classification
accuracies are slightly lower. The July image shows the
maximum decrease of 5.4%.
The multi-temporal masking classification yields better
results (Table 3) than both the all bands classification and the
first 4 PCs classification of the July and August images
alone. The overall accuracy of the masking classification was
computed as 81.3%. When compared to the classification of
the July and August images alone the improvement in overall
accuracy of more than 10% is evident. This improvement in
accuracy with the masking procedure is due to the use of
different spectral responses of the crops over a period of time
according to phenological evolution. The accuracy of the
clover, residue, sugar beet, rice, tomato, and corn classes
were quite high. The producer’s and user’s accuracies of
clover were 100%. Residue had a user’s accuracy of 100%
and a producer’s accuracy of 93.1%. It was found that of the
174 reference fields, only 12 were omitted from the residue
category. The user’s and producer’s accuracies of sugar beet
were 100% and 80% respectively. It is evident that sugar beet
does not exhibit commission error. However, the exclusion of
21 fields out of 105 from this class resulted in 20% omission
error. The user’s and producer’s accuracies of rice were also
computed as 100% and 80% respectively. Of the total ten
fields, eight were correctly classified through masking
classification procedure and two were omitted from rice.
Tomato had a producer’s accuracy of 86.6% and a user’s
accuracy of 77.3%. Of the 291 reference tomato fields, 252
were correctly classified. Tomato is most often confused with
corn and pepper, and additional imagery can improve
5. classification accuracy. For corn, the masking procedure
resulted in a producer’s accuracy of 77.4% and a user’s
accuracy of 86.2%. Pasture had a very high producer’s
accuracy (100%) and a rather low user’s accuracy (66.7%). It
was observed that four residue fields, one tomato field, and
two cauliflower fields were improperly included in the
pasture class. Watermelon exhibits a relatively low
producer’s accuracy of 64.3% and a user’s accuracy of 75%.
Of the 14 reference fields, 5 were omitted from this class
resulting an omission error of 35.7%. The user’s accuracy of
cauliflower (81%) is relatively high when compared to other
classes. However, this class illustrates a considerably high
error of omission (46.9%) which is caused by the exclusion
of 15 fields from this category. Both pepper and bare soil
exhibit low classification accuracy. While the producer’s and
user’s accuracies of pepper were 52.8% and 38.4%
respectively, bare soil had the lowest user’s accuracy of
23.5% and a considerably low producer’s accuracy of 57.1%.
It is evident that the spectral signatures of pepper and both
tomato and corn greately overlap in the images used in this
study.
In comparison with the all bands classification of the August
image, the multi-temporal masking classification shows
10.5% increase in overall accuracy. Clover exhibits the
highest increase of 50% in the producer’s accuracy. A
significant increase in the producer’s accuracy is also evident
for bare soil, residue, and sugar beet. However, while residue
and sugar beet exhibit 1.1% and 4.7% increase in the user’s
accuracy respectively, bare soil shows 19.4% decrease. It
appears that the number of the fields that were improperly
included in the bare soil class increased with the masking
procedure. Among the classes which do not exhibit a
significant improvement are corn, tomato, pepper, and
watermelon. A significant increase (26.7%) in the user’s
accuracy of pasture indicates that some of the fields that were
improperly included in pasture in all bands classification of
the August image were correctly classified with the
sequential masking procedure. Cauliflower presents the
highest increase of 54% in the user’s accuracy. A significant
improvement in the user’s accuracy (25%) is also evident for
rice. However, both cauliflower and rice exhibit a decrease in
the producer’s accuracy. As an overall, the results show that
the masking procedure improve the accuracies and the
increase is significant for several classes.
6. CONCLUSIONS
The sequential masking classification of Landsat7 ETM+
images acquired in three different dates (May, July, and
August 2000), coupled with field-based analysis, to identify
summer crops proved to be better than the uni-temporal
classifications. The overall accuracy for the all bands
classification of the May image (88.9%) was the highest. This
is attributed to the fact that the number of classes are less on
May image than that of July and August images and the
classes are spectrally distinct from each other. The overall
accuracies for the all bands classification of the July and
August images were found to be 66.8% and 70.8%
respectively. The overall accuracies for the first 4 PCs
classifications were found to be slightly lower than that of all
bands classifications.
In comparison with the uni-temporal classification results, the
sequential masking classification performs better, giving an
overall accuracy of 81.3%. The use of sequential masking
procedure improved the overall accuracies of the
classifications of the July and August images performed
alone by more than 10%. It appears that the masking
technique has overcome the problems caused by the spectral
overlaps between the classes. The level of classification
accuracy achieved in this study through masking procedure is
possibly high enough for crop mapping from Landsat7 ETM+
images, and better results may be achieved if additional
images are used.
Acknowledgements
The authors are grateful to State Planning Organization
(DPT) of Turkey for supporting this project.
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