One of our published researches in ACRS 31st in Hanoi.
It has been used for our project in processing optical satellite imagery to detect environmental pollution.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered being accurate or ground truth data.
I prepared this presentation for the interactive class of the course "Arcgis and Remote Sensing" organized by the Research Society, Bangladesh.
SBAS-DInSAR processing on the ESA Geohazards Exploitation PlatformEmmanuel Mathot
In the context of space-borne geodetic techniques, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated its high performance in measuring surface displacements in different conditions and scenarios, both natural and anthropic. In particular, the advanced DInSAR time series processing method referred to as Small BAseline Subset (SBAS), that allows studying both the spatial and temporal variability of the surface displacements, has proven to be particularly suitable in different contexts, as for natural hazards (volcanoes, earthquakes and landslides) and human-induced deformation (subsidence due to aquifer exploitation, mining operations, and building of large infrastructures). Recently, an efficient implementation of this algorithm (referred to as P-SBAS approach) has been fully integrated within the ESA’s Grid Processing on Demand (G-POD) environment, which is part of the [Geohazards Thematic Exploitation Platform (GEP)](https://geohazards-tep.eo.esa.int/#!) of ESA. The GEP is devoted to the exploitation of EO data resources in the context of the Geohazard Supersites & Natural Laboratories as well as on the CEOS Pilots on Seismic Hazards and Volcanoes. The GEP is sourced with elements, data and processing, including P-SBAS, relevant to the geohazards theme. The integration of the P-SBAS algorithm within GEP resulted in a web-based tool freely available to the scientific community. This tool allows users to process, from their own laptops, the European SAR data archives (ERS, ENVISAT and Sentinel-1) for obtaining surface displacement maps and time series in a completely unsupervised way, without caring about data download and processing facility procurements. The workshop is organized in four parts. First, a short overview on the DInSAR processing methods allowing retrieving mean surface deformation maps and displacement time series will be provided, with a specific focus on the SBAS-DInSAR technique. Secondly, the GEP and G-POD environments will be introduced and the P-SBAS web tool will be presented. The third and the fourth parts are dedicated to the advanced features and to case studies and results achieved via the web tool, respectively.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Spatial analysis & interpolation in ARC GISKU Leuven
In ArcGIS, a data model describes the thematic layers used in the applications (for example, hamburger stands, roads, and counties); their spatial representation (for example, point, line, or polygon); their attributes; their integrity rules and relationships (for example, counties must nest within states).
This presentation will give a simple overview of image classification technique using difference type software focusing on object-based image classification and segmentation.
A ~25 slide presentation that explains the underlying principles and some applications of InSAR, with a particular focus on the measurement of deformation due to earthquakes. The presentation could be used in a lecture or lab setting, or provided to students for review out of class. The slides are annotated with additional background information designed to assist instructors.
Explanation of very simple methods for atmospheric corrections and an example adapted from a paper of the Dept. of Thermodynamics, University of Valencia, Spain.
COMPUTATIONALLY EFFICIENT TWO STAGE SEQUENTIAL FRAMEWORK FOR STEREO MATCHINGijfcstjournal
Almost all the existing stereo algorithms fall under a common assumption that corresponding color or
intensity values will be similar like one another. On the other hand, it is also not that true in practice
where the image color or intensity values are regularly affected by different radiometric factors like
illumination direction, change in image device, illuminant color and so on. For this issue, the information
about the raw color of the images which is recorded by the camera should not depend on it totally, and
also the common assumptions on color consistency doesn’t influence good (great) between the stereo
images in real scenario. Therefore, most of the conventional stereo algorithms can be seriously degraded
in terms of performance under radiometric variations. In this work, we intend to develop a new stereo
matching algorithm which will be insensitive to change in radiometric conditions between stereo pairs i.e.
left image as well as right image. Unlike the other stereo algorithms, we propose a computationally
efficient two stage sequential framework for stereo matching which can handle the various radiometric
variations between the stereo pairs.Experimental results proves that the proposed method outperforms
extremely well compare to other state of the art stereo methods under change in various radiometric
conditions for a given stereo pair and it is also found from the results that the execution time is less
compare to existing methods.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered being accurate or ground truth data.
I prepared this presentation for the interactive class of the course "Arcgis and Remote Sensing" organized by the Research Society, Bangladesh.
SBAS-DInSAR processing on the ESA Geohazards Exploitation PlatformEmmanuel Mathot
In the context of space-borne geodetic techniques, Differential Synthetic Aperture Radar Interferometry (DInSAR) has demonstrated its high performance in measuring surface displacements in different conditions and scenarios, both natural and anthropic. In particular, the advanced DInSAR time series processing method referred to as Small BAseline Subset (SBAS), that allows studying both the spatial and temporal variability of the surface displacements, has proven to be particularly suitable in different contexts, as for natural hazards (volcanoes, earthquakes and landslides) and human-induced deformation (subsidence due to aquifer exploitation, mining operations, and building of large infrastructures). Recently, an efficient implementation of this algorithm (referred to as P-SBAS approach) has been fully integrated within the ESA’s Grid Processing on Demand (G-POD) environment, which is part of the [Geohazards Thematic Exploitation Platform (GEP)](https://geohazards-tep.eo.esa.int/#!) of ESA. The GEP is devoted to the exploitation of EO data resources in the context of the Geohazard Supersites & Natural Laboratories as well as on the CEOS Pilots on Seismic Hazards and Volcanoes. The GEP is sourced with elements, data and processing, including P-SBAS, relevant to the geohazards theme. The integration of the P-SBAS algorithm within GEP resulted in a web-based tool freely available to the scientific community. This tool allows users to process, from their own laptops, the European SAR data archives (ERS, ENVISAT and Sentinel-1) for obtaining surface displacement maps and time series in a completely unsupervised way, without caring about data download and processing facility procurements. The workshop is organized in four parts. First, a short overview on the DInSAR processing methods allowing retrieving mean surface deformation maps and displacement time series will be provided, with a specific focus on the SBAS-DInSAR technique. Secondly, the GEP and G-POD environments will be introduced and the P-SBAS web tool will be presented. The third and the fourth parts are dedicated to the advanced features and to case studies and results achieved via the web tool, respectively.
In the context of remote sensing, change detection refers to the process of identifying differences in the state of land features by observing them at different times. This process can be accomplished either manually (i.e., by hand) or with the aid of remote sensing software. Manual interpretation of change from satellite images or aerial photos involves an observer or analyst defining areas of interest and comparing them between images from two dates. This may be accomplished either on-screen (such as in a GIS) or on paper. When analyzing aerial photographs, a stereoscope which allows for two spatially-overlapping photos to be displayed in 3D, can aid photo interpretation. Manual image interpretation works well when assessing change between discrete classes (forest openings, land use and land cover maps) or when changes are large (e.g., heavy mechanized maneuver damage, engineering training impacts). Manual image interpretation is also an option when trying to determine change using images or photos from different sources (comparing historic aerial photographs to current satellite imagery).
Automated methods of remote sensing change detection usually are of two forms: post-classification change detection and image differencing using band ratios. In post-classification change detection, the images from each time period are classified using the same classification scheme into a number of discrete categories like land cover types. The two (or more) classifications are compared and the area that is classified the same or different is tallied. With image differencing, a band ratio such as NDVI is constructed from each input image, and the difference is taken between the band ratios of different times. In the case of differencing NDVI images, positive output values may indicate an increase in vegetation, negative values a decrease in vegetation, and values near zero no change. With either post-classification or image differencing change detection, it is necessary to specify a threshold below which differences between the two images is considered to be non-significant. The specification of thresholds is critical to the results of change detection analysis and usually must be found through an iterative process.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Spatial analysis & interpolation in ARC GISKU Leuven
In ArcGIS, a data model describes the thematic layers used in the applications (for example, hamburger stands, roads, and counties); their spatial representation (for example, point, line, or polygon); their attributes; their integrity rules and relationships (for example, counties must nest within states).
This presentation will give a simple overview of image classification technique using difference type software focusing on object-based image classification and segmentation.
A ~25 slide presentation that explains the underlying principles and some applications of InSAR, with a particular focus on the measurement of deformation due to earthquakes. The presentation could be used in a lecture or lab setting, or provided to students for review out of class. The slides are annotated with additional background information designed to assist instructors.
Explanation of very simple methods for atmospheric corrections and an example adapted from a paper of the Dept. of Thermodynamics, University of Valencia, Spain.
COMPUTATIONALLY EFFICIENT TWO STAGE SEQUENTIAL FRAMEWORK FOR STEREO MATCHINGijfcstjournal
Almost all the existing stereo algorithms fall under a common assumption that corresponding color or
intensity values will be similar like one another. On the other hand, it is also not that true in practice
where the image color or intensity values are regularly affected by different radiometric factors like
illumination direction, change in image device, illuminant color and so on. For this issue, the information
about the raw color of the images which is recorded by the camera should not depend on it totally, and
also the common assumptions on color consistency doesn’t influence good (great) between the stereo
images in real scenario. Therefore, most of the conventional stereo algorithms can be seriously degraded
in terms of performance under radiometric variations. In this work, we intend to develop a new stereo
matching algorithm which will be insensitive to change in radiometric conditions between stereo pairs i.e.
left image as well as right image. Unlike the other stereo algorithms, we propose a computationally
efficient two stage sequential framework for stereo matching which can handle the various radiometric
variations between the stereo pairs.Experimental results proves that the proposed method outperforms
extremely well compare to other state of the art stereo methods under change in various radiometric
conditions for a given stereo pair and it is also found from the results that the execution time is less
compare to existing methods.
this presentation briefly describes the digital image processing and its various procedures and techniques which include image correction or rectification with remote sensing data/ images. it also contains various image classification techniques.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
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.
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
A NOVEL APPROACH FOR SEGMENTATION OF SECTOR SCAN SONAR IMAGES USING ADAPTIVE ...ijistjournal
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre- treatment required in any SONAR images analysis system. The results obtained from the proposed method were compared quantitatively and qualitatively with the results obtained from the other speckle reduction techniques and demonstrate its higher performance for speckle reduction in the SONAR images.
With the improvements in Image acquisition systems there is an increasing concentration in the direction of
High Dynamic Range (HDR) images where the amount of intensity levels varies among 2 to 10,000. With these
numerous intensity levels the exact representation of luminance variations is entirely possible. But, because the
normal display devices are shaped to exhibit Low Dynamic Range (LDR) images, there is necessary to translate
HDR images to LDR images without down significant image structures in HDR images. In this paper four TMOs
like Reinhard, Gamma and color correction TMOs are evaluated .In this paper two novel TMOs are projected.
Keywords — HDR, LDR, Tone mapping, Gamma correction.
Data-Driven Motion Estimation With Spatial AdaptationCSCJournals
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
Satellite image processing is a technique to enhance raw images received from cameras or sensors placed on satellites, space probes and aircrafts or pictures taken in normal day to day life in various applications. The process of creating thematic maps as spatial distribution of particular information. These are structured by Spectral Bands. These have constant density and when they overlap their densities get added. It performs image analysis on multiple scale images and catches the comprehensive information of system for different application. Examples of themes are soil, vegetation, water-depth and air. The supervising of such critical events requires a huge volume of surveillance data and extremely powerful real time processing for infrastructure
Similar to Evaluating effectiveness of radiometric correction for optical satellite image using statistical parameters (20)
Evaluating effectiveness of radiometric correction for optical satellite image using statistical parameters
1. EVALUATING EFFECTIVENESS OF RADIOMETRIC CORRECTION
FOR OPTICAL SATELLITE IMAGE
USING STATISTICAL PARAMETERS
Dr Hab. Luong Chinh Ke 1 and BA. Nguyen Le Dang2
1
Research Center for Remote Sensing Science and Technology
National Center for Remote Sensing, 108 Chua Lang, Dong Da, Ha Noi, Vietnam
Email: lchinhke@gmail.com
2
Research Center for Remote Sensing Science and Technology
National Center for Remote Sensing, 108 Chua Lang, Dong Da, Ha Noi, Vietnam
Email: dangnguyen2211@gmail.com
ABSTRACT
Optical satellite image (OSI) in original form (raw image) is an image of optical gray values of each
pixel, characterized by DN quantity (Digital Number). DN values not only contain the signal reflected directly
from the surface of the object but also contain other diffusing and reflecting signals in atmospheric
environment. In many practical applications image data in the form of the original DN image can not use
directly, but need to filter the noise signals and then convert it into image reflected of objects in the ground
surface.
In order to increase the accuracy and reliability of products created from satellite images such as image
classification, change detection of land use/land cover etc., the processing of radiometric corrections from DN
(raw) image into reflected image in ground surface have to be done. The present article uses statistical
parameters of digital image calculated before and after radiometric corrections to compare and evaluate its
quality.
1. INTRODUCTION
Analyzing satellite image have an important role in monitoring and modeling
environmental problems. Repeated observation of a given area over time yields potential for
many forms of change detection analysis. These repeated observations are confounded in
terms of radiometric consistency due to changes in sensor calibration over time, differences
in illumination, observation angles and variation in atmospheric effects. Radiometric
correction of satellite image data comprises of two issues: absolute and relative correction
(Seema and Udhav, 2010). Absolute radiometric correction converts the digital number of a
pixel to a percentage reflectance value using established transformation equations (Chavez
and Mackinnon, 1994; Caselles and Garcia, 1989). Relative radiometric correction
normalizes multiple satellite scenes to each other. Most forms of absolute radiometric
correction rely on sensor calibration coefficients, atmospheric correction algorithms, and
illumination and observation geometry coefficients. These data are used in a radiative transfer
model to correct the imagery. Relative radiometric correction has several advantages over
absolute radiometric correction. The methodology is usually simpler, requires less computer
operating time and less theoretical understanding (Hall, et al., 1991; Schott, et al., 1988).
International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2010
2. Our purpose of this study describes how to calculate the reflectance at surface and
reflectance at the top of atmosphere by certain algorithms and assess data accuracy after
calculating.
2. BACKGROUND
The sun's rays carry energy with five different routes, finally going on the satellite’s
sensor. Figure 1 illustrates the paths of the sun's rays with the characteristic quantities back to
sensor (Jesen, 1996).
Figure 1. The paths of sun's rays Figure 2. The process of transformation
on the sensor. from DN image → R → rTOA → r sur .
Figure 1 clearly shows the DN values (Digital Number) of the scene with signal L
which were recorded by sensor will be the sum of "real" signal, Ls (direct reflection from
objects on ground surface to sensor) and the "noise" signal, Lp (the reflection, scattering by
the surrounding environment).
L = Ls + Lp (1)
The nature of the problem of radiometric correction of satellite image is to remove the
noise signal Lp. Assuming that the topography surface is Lambert surface (smooth surface,
height difference not significant), the radiometric correction should be done are:
- Correction of spectrum sensor based on its calibrated parameters;
- Atmosphere correction.
Relationship between radiance spectrum of the four image positions in the space of the
same area is described in Figure 2. Four image positions are as follows:
- Image No.1 is the original (raw) image with pixel DN values (past-sensor image).
- Image No.2 is the result of sensor’s spectrum correction; actually converting DN
image (past-sensor image) into the radiance image R (at-sensor image).
- Image No.3 is the conversion result of radiance image R into reflected image at the
top of atmosphere, (denote ρTOA image).
- Image No.4 is the result of continuing a conversion of ρTOA image into the reflected
image of the surface topography (denote ρSUR image).
Evaluating Effectiveness of Radiometric Correction for Optical Satellite Image using Statistical Parameters
3. 3. EXPERIMENTAL PART
3.1 Image data
Input Data is a satellite image Landsat ETM+ taken on 29/9/2001 of Cam Pha - Quang
Ninh areas (Figure 2). Image parameters of useful Landsat scene in Cam Pha are presented in
Table 1.
Table 1. Scene of Landsat ETM+ satellite image of Cam Pha area
Parameters of Landsat ETM+ image
Acquisition date 29/09/2001
Datum grid UTM
Solar angle (degree) 56.2178576
band 1 band 2 band 3 band 4
Radiance values Lmax (W/m2*ster*µm) 191.60 196.50 152.90 241.10
Radiance values Lmin (W/m2*ster*µm) -6.200 -6.400 -5.00 -5.100
Sun radiance value (W/m2*µm) 1969.00 1840.00 1551.00 1044.00
Experimental part has been conducted according to scheme in Figure 2. Two products
of image category also have been created:
- True color (RBG) combined images;
- NDVI vegetation index images.
3.2 Products of true color (RBG) combined image
The number of color combined images are four images corresponding to the four
positions in space as introduced in Figure 2. Creating the image sequence is described as
written in paragraph 2.
Output four color combined images corresponding to the four image positions in space
as DN (1), R (2) (Radiance), rTOA (3), r sur (4) image is shown in Figure 3.
The statistical parameter values of the four RBG color combined image were calculated
and recorded in Table 2; where "Mean" is the average value of images (image’s mathematical
expectation); "Var" is image variance and CV is image’s change variance index of image,
calculated by the formula.
(CV = var / Mean ) (2)
International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2010
4. a. DN image (past-sensor) b. R image (at-sensor)
c. r TOA image (at TOA) d. r SUR image (in surface)
Figure 3. Four color combinations of images
Table 2. Statistical parameters of four color RBG combined
Statistical
Parameters
DN (1) R (2) rTOA (3) r sur (4)
Mean 74.5 53.339125 0.0363000625 0.243616
Var 107.316625 67.944025 0.000416695625 0.001053128
CV 0.13905 0.154536 0.56234 0.133209
Through Figure 3 and Table 2 show: Although observed with eye we hardly detect the
difference between images (1), (2), (3) and (4), but the quantitative values in Table 1 reflects
the nature of these images very different. We have commented:
- DN image or past-sensor image with No (1) has CV smaller than 1 but have largest
variance with respect to others.
- At-sensor image or radiance image, R, with No (2) (after correcting sensor spectrum)
has CV approximate to CV of DN image (1), but its variance is nearly two times
smaller than variance of DN image (1).
- Image at the top of atmosphere, r Sur , with No (3) has CV larger than CV of DN (1),
but still smaller than 1. Its variance is very small (considered equal to 0).
- The reflected image on the topography surface, ( r Sur ), with No (4) has CV
approximately equal to CV of DN image (1), but its variance value, Var, is 100
thousand times smaller than DN image (1).
Evaluating Effectiveness of Radiometric Correction for Optical Satellite Image using Statistical Parameters
5. Clearly, reflected image on the topography surface with r Sur (4) has the highest
quality. From evaluating of color (RBG) combined image, we concluded that by “feeling” in
the computer screen, our eyes may misunderstand the combination of color images without a
difference, but with statistical models of image, we see the nature of this type of images
completely different in quality.
3.3. Products of NDVI vegetation index image
Going deeply in detail, we use images of vegetation index (VI) to analyze four types of
image such as DN, R, r TOA , and r SUR . Usually images of vegetation use only NDVI defined
as follows:
NIR - RED
NDVI = (3)
NIR + RED
We have four types of vegetation index NDVI images such as (DN), NDVI (R), NDVI
( r TOA ) and NDVI ( r SUR ) that corresponding to 4 position in space (see Figure 2). It means
vegetation index images were created by pixel DN values, (past-sensor); R (at-sensor image);
reflected image at the top of atmosphere, r TOA and reflected image in the topography
surface, r SUR , respectively. Four DNVI images are shown in Figure 4.
a. Past-sensor NDVI image b. At-sensor NDVI images
c. NDVI from r TOA image d. NDVI from image r sur
Figure 4. Four NDVI images
The statistical parameter values of the four NDVI images were calculated and recorded
in Table 3 (see footnote of Table 2).
International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2010
6. Table 3. Statistical parameters of four NDVI images
Parameters NDVI (DN) NDVI (R) NDVI ( r TOA ) NDVI ( r sur )
Mean 0.25 0.1552877775 0.33104825 -0.99847675
Var 10.9221465 0.04456275 0.037273225 0.0000004307545
CV 13.21947 1.359404 0.583186 0.00065732
Deeply analysis based on vegetation index images we see, by a “feeling” of the human
eye; we can recognize the difference between the NDVI images. By "quantitative values"
through image statistical parameters in Table 3, as confirmed differences in the nature of the
four types of NDVI images. NDVI images ( r sur ) is regarded as the variance of 0 with CV
variable is the smallest. This is the image of the highest quality. There should also be added
that the value of "Mean" of NDVI ( r sur ) approximately equal to -1 because this area is coal
mining, the vegetation factor is very low. Value "Mean" of the image NDVI (DN) is the
"virtual" value because the value of "Var" and the variable CV are very large compared to
other images. In fact, using NDVI (DN) to classify images in research of land cover/land use
change, as well as in some other tasks will have very low reliability.
In short, radiometric correction for Lambert terrain from DN to image reflected image
in the topography surface is needed to practical applications in the thematic maps of
monitoring resources and environment in order to increase the accuracies and reliabilities of
useful products.
4. REFERENCES
Chavez, P.S. and D.J. Mackinnon, 1994. Automatic detection of vegetation changes in the
Southwestern United States using remotely sensed images. Photogramm. Eng. Remote Sens.,
60: 571-583.
Conese C.; M. A. i l a b e r, 1993. Topographic Normalization of TM Scenes through the Use of an
Atmospheric Correction end Digital Terrain Model. Photogrammetric Engineering & Remote
Sensing, Vol. 59, No. 12, 1993, 1745-1753.
Caselles, V. and M.J.L. Garcia, 1989. An alternative simple approach to estimate atmospheric
correction in multispectral studies. Int. J. Remote Sens., 10: 1127-1134.
Elvidge, C.D., D. Yuan, R.D. Werackoon, R.S. Lunetta, 1995. Relative radiometric normalization of
landsat Multispectral Scanner (MSS) data using an automated scattergram controlled
regression. Photogramm. Eng. Remote Sens., 61: 1255-1260.
Hall, F.G., D.E. Strebel, J.E. Nickeson and S.J. Goetz, 1991. Radiometric rectification toward a
common radiometric response among multidate, multisensor images. Remote Sens. Environ.,
35: 11-27.
Jesen J. R., 1996, Introduction Digital Image Orocessing, A Remote Sensing Perspective, Second
Edition, Prentic Hall, New Jersey.
Schott, J.R., C. Salvaggio and W.J. Volchok, 1988. Radiometric scene normalization using
pseudoinvariant features. Remote Sens. Environ., 26: 1-16.
Seema Gore Biday and Udhav Bhosle, 2010, Radiometric correction of multitemporal satellite
imagery. Journal of Computer Science 6(9), 940-949.
Evaluating Effectiveness of Radiometric Correction for Optical Satellite Image using Statistical Parameters