Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slide for study session given by Dr. Enrico Rinaldi at Arithmer inc.
It is a summary of recent methods for real-time instance segmentation "YOLACT", which is especially useful in robotics.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
Slide for study session given by Dr. Enrico Rinaldi at Arithmer inc.
It is a summary of recent methods for real-time instance segmentation "YOLACT", which is especially useful in robotics.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Yinyin Liu presents a model for object detection and localization, called Fast-RCNN. She will show how to introduce a ROI pooling layer into neon, and how to add the PASCAL VOC dataset to interface with model training and inference. Lastly, Yinyin will run through a demo on how to apply the trained model to detect new objects.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Our goal is to create a web application that would give insights to its user about the crime scenario and its various aspects in Chicago.
Our application will contain:
A search box/drop down list where user can select a district.
Geospatial analysis using ArcGIS maps and visualizations that are embedded into the web app which will be dynamically updated to show most interesting patterns or heat maps for that district.
Statistical analysis and visualizations on historical data to the user.
Prediction of the date when the next crime will happen and its probability.
Spectral Clustering and Vantage Point Indexing for Efficient Data Retrieval IJECEIAES
Data mining is an essential process for identifying the patterns in large datasets through machine learning techniques and database systems. Clustering of high dimensional data is becoming very challenging process due to curse of dimensionality. In addition, space complexity and data retrieval performance was not improved. In order to overcome the limitation, Spectral Clustering Based VP Tree Indexing Technique is introduced. The technique clusters and indexes the densely populated high dimensional data points for effective data retrieval based on user query. A Normalized Spectral Clustering Algorithm is used to group similar high dimensional data points. After that, Vantage Point Tree is constructed for indexing the clustered data points with minimum space complexity. At last, indexed data gets retrieved based on user query using Vantage Point Tree based Data Retrieval Algorithm. This in turn helps to improve true positive rate with minimum retrieval time. The performance is measured in terms of space complexity, true positive rate and data retrieval time with El Nino weather data sets from UCI Machine Learning Repository. An experimental result shows that the proposed technique is able to reduce the space complexity by 33% and also reduces the data retrieval time by 24% when compared to state-of-the-artworks.
In this project, we propose methods for semantic segmentation with the deep learning state-of-the-art models. Moreover,
we want to filterize the segmentation to the specific object in specific application. Instead of concentrating on unnecessary objects we
can focus on special ones and make it more specialize and effecient for special purposes. Furtheromore, In this project, we leverage
models that are suitable for face segmentation. The models that are used in this project are Mask-RCNN and DeepLabv3. The
experimental results clearly indicate that how illustrated approach are efficient and robust in the segmentation task to the previous work
in the field of segmentation. These models are reached to 74.4 and 86.6 precision of Mean of Intersection over Union. The visual
Results of the models are shown in Appendix part.
Artificial Intelligence for Vision: A walkthrough of recent breakthroughsNikolas Markou
we embark on a quest to uncover the fascinating evolution of computer vision, from humble beginnings to the cutting-edge marvels of Vision Transformers.
Performance evaluation of transfer learning based deep convolutional neural n...IJECEIAES
Deep learning (DL) techniques are effective in various applications, such as parameter estimation, image classification, recognition, and anomaly detection. They excel with abundant training data but struggle with limited data. To overcome this, transfer learning is commonly used, leveraging complex learning abilities, saving time, and handling limited labeled data. This study assesses a transfer learning (TL)-based pre-trained “deep convolutional neural network (DCNN)” for classifying land use land cover using a limited and imbalanced dataset of fused spectro-temporal data. It compares the performance of shallow artificial neural networks (ANNs) and deep convolutional neural networks, utilizing multi-spectral sentinel-2 and high-resolution planet scope data. Both machine learning and deep learning algorithms successfully classified the fused data, but the transfer learning-based deep convolutional neural network outperformed the artificial neural network. The evaluation considered a weighted average of F1-score and overall classification accuracy. The transfer learning-based convolutional neural network achieved a weighted average F1-score of 0.92 and a classification accuracy of 0.93, while the artificial neural network achieved a weighted average F1-score of 0.87 and a classification accuracy of 0.89. These results highlight the superior performance of the transfer learned convolutional neural network on a limited and imbalanced dataset compared to the traditional artificial neural network algorithm.
Satellite Image Classification with Deep Learning Surveyijtsrd
Satellite imagery is important for many applications including disaster response, law enforcement and environmental monitoring etc. These applications require the manual identification of objects in the imagery. Because the geographic area to be covered is very large and the analysts available to conduct the searches are few, thus an automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a part of broader family of machine learning methods that have shown promise for the automation of such tasks. It has achieved success in image understanding by means that of convolutional neural networks. The problem of object and facility recognition in satellite imagery is considered. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. Roshni Rajendran | Liji Samuel ""Satellite Image Classification with Deep Learning: Survey"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30031.pdf
Paper Url : https://www.ijtsrd.com/engineering/computer-engineering/30031/satellite-image-classification-with-deep-learning-survey/roshni-rajendran
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Unsupervised Building Extraction from High Resolution Satellite Images Irresp...CSCJournals
Extraction of geospatial data from the photogrammetric sensing images becomes more and more important with the advances in the technology. Today Geographic Information Systems are used in a large variety of applications in engineering, city planning and social sciences. Geospatial data like roads, buildings and rivers are the most critical feeds of a GIS database. However, extracting buildings is one of the most complex and challenging tasks as there exist a lot of inhomogeneity due to varying hierarchy. The variety of the type of buildings and also the shapes of rooftops are very inconstant. Also in some areas, the buildings are placed irregularly or too close to each other. For these reasons, even by using high resolution IKONOS and QuickBird satellite imagery the quality percentage of building extraction is very less. This paper proposes a solution to the problem of automatic and unsupervised extraction of building features irrespective of rooftop structures in multispectral satellite images. The algorithm instead of detecting the region of interest, eliminates areas other than the region of interest which extract the rooftops completely irrespective of their shapes. Extensive tests indicate that the methodology performs well to extract buildings in complex environments.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Similar to Analysis by semantic segmentation of Multispectral satellite imagery using deep learning (20)
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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/
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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.
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.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Hori...
Analysis by semantic segmentation of Multispectral satellite imagery using deep learning
1. Analysis of Multispectral Satellite Imagery
using Deep Learning
Internal Guide : Dr. Raghunandan S, NMIT
M. Tech. (VLSI Design and Embedded Systems)
By –Yogesh SAwate
NMIT-1NT16LVS10
Nitte Meenakshi Institute of Technology, Bangalore
2. Outline
• Aim of project
• My approach
• Semantic segmentation
• Gathering data
• CNN Models
• Implementation method
• Topics/Tools learning
• Project plan
• References
9-Aug-18 Nitte Meenakshi Institute of Technology 2
3. Aim of Project
• To create a deep learning model which can be used to
identify vegetation, buildings, water and roads in the
multispectral satellite imagery.
9-Aug-18 Nitte Meenakshi Institute of Technology 3
4. My Approach
• Semantic segmentation
• Gathering Data
• Preparing the dataset
• Standard model
• My Models
• Evaluation of models
9-Aug-18 Nitte Meenakshi Institute of Technology 4
5. Semantic Segmentation
9-Aug-18 Nitte Meenakshi Institute of Technology 5
• Semantic segmentation, in simple words, is pixel wise
representation of classes
• It attempts to partition the image into semantically
meaningful parts, and to classify each part into one of the
pre-determined classes
• Semantic segmentation helps in proper understanding of
scenes for computer vision
RGB image SS image
6. Gathering data - Satellite Images
9-Aug-18 Nitte Meenakshi Institute of Technology 6
Blue band
Green band
Red band
NIR band
7. RGB Image
9-Aug-18 Nitte Meenakshi Institute of Technology 7
True Colour Composition
Red band – Red, Green band – Green, Blue band – Blue
Resolution- 2m, Pixel resolution – 2713*2032
8. Composite Image
9-Aug-18 Nitte Meenakshi Institute of Technology 8
False Colour Composition
NIR band – Red, Green band – Green, Blue band – Blue
Resolution- 2m, Pixel resolution – 2713*2032
9. Preparing dataset- Ground Truth
9-Aug-18 Nitte Meenakshi Institute of Technology 9
Ground Truth:
Red–Trees, Blue–Buildings, White–Water body, Cyan-Roads,
Resolution- 2m, Pixel resolution – 2713*2032
10. Patches
• To make a complete dataset for training and evaluation, the
multispectral images are converted into patches
• 577 patches (256*256) of each bands and Ground truth were
made for this project
• 500 images for training and 77 for evaluation were separated
• Augmentations like rotations and mirroring were done for better
learning
9-Aug-18 Nitte Meenakshi Institute of Technology 10
12. Convolutional Neural Network
• The Convolutional layer has deep layers of filters, it can directly
access images
• Each Convolutional layer gives feature maps, number of
channels of feature maps is depending upon number of filters
9-Aug-18 Nitte Meenakshi Institute of Technology 12
13. VGG-13
9-Aug-18 Nitte Meenakshi Institute of Technology 13
• VGG-16 is one of the standard
architectures in deep learning
• I removed fully connected 3
layers
• Making it useful for semantic
segmentation
16. Evaluation
• Evaluation of a model is done by taking the total class elements
• Class elements include True positives and negatives, False
positives and negatives
9-Aug-18 Nitte Meenakshi Institute of Technology 16
True
positives
False
positives
False negatives True negatives
Relevant elements
17. Scores description
• Precision scores- how many selected items are relevant?
• Recall scores- how many relevant items are selected?
• F1 scores- It is harmonic average of Precision and Recall
9-Aug-18 Nitte Meenakshi Institute of Technology 17
20. Precision score
The graph shows Precision score comparison between VGG-13,
FCN and FCN-skip. The FCN-skip out performs VGG-13and
FCN with 0.9724018 score. Therefore FCN-skip model has
higher positive predictive value.
9-Aug-18 Nitte Meenakshi Institute of Technology 20
21. Recall score
The graph shows Recall score comparison between VGG-13,
FCN and FCN-skip. The FCN-skip out performs VGG-13and
FCN with 0.9541074 score. The recall score shows the
sensitivity of model. As we can see FCN-skip has higher
sensitivity as compared VGG-13 and FCN.
9-Aug-18 Nitte Meenakshi Institute of Technology 21
22. F1-score
The graph shows the F1-score comparison between VGG-13,
FCN and FCN-skip. The FCN-skip out performs VGG-13and
FCN with 0.944082 score. F1 score shows the accuracy of the
model. As we can see FCN-skip architecture is efficient.
9-Aug-18 Nitte Meenakshi Institute of Technology 22
23. Reference
[1] R. Kemker, C. Salvaggio, and C. Kanan, “Algorithms for semantic segmentation of multispectral remote sensing
imagery using deep learning,” ISPRS Journal of Photogrammetry and Remote Sensing, 2018.
https://arxiv.org/pdf/1703.06452.pdf
[2] J. E. Ball, D. T. Anderson, and C. S. Chan, “Comprehensive survey of deep learning in remote sensing: theories,
tools, and challenges for the community,” Journal of Applied Remote Sensing, vol. 11, no. 4, p. 042609, 2017.
[3] C. Luo, J. Wang, G. Feng, S. Xu, and S. Wang, “Do deep convolutional neural ;networks really need to be deep
when applied for remote scene classification?” Journal of Applied Remote Sensing, vol. 11, no. 4, p. 042613,
2017.
[4] T. Ishii, E. Simo-Serra, S. Iizuka, Y. Mochizuki, A. Sugimoto, H. Ishikawa, and R. Nakamura, “Detection by
classification of buildings in multispectral satellite imagery,” in Pattern Recognition (ICPR), 2016 23rd
International Conference on. IEEE, 2016, pp. 3344–3349.
[5] Y. Tarabalka, J. Benediktsson, J. Chanussot, and J. Tilton, “multiple spectralspatial classification approach for
hyperspectral data, ieee trans. on geoscience and remote sensing, under review.” Classification of hyperspectral
data using spectral-spatial approaches, p. 169.
[6] M. Teichmann, M. Weber, M. Zoellner, R. Cipolla, and R. Urtasun, “Multinet: Real-time joint semantic
reasoning for autonomous driving,” arXiv preprint: arXiv:1612.07695, 2016.
https://arxiv.org/pdf/1612.07695.pdf
[7] X. Cao, F. Zhou, L. Xu, D. Meng, Z. Xu, and J. Paisley, “Hyperspectral image classification with markov
random fields and a convolutional neural network,” IEEE Transactions on Image Processing, vol. 27, no. 5, pp.
2354–2367, 2018.
[8] Y. Tarabalka, J. Chanussot, J. A. Benediktsson, J. Angulo, and M. Fauvel, “Segmentation and classification of
hyperspectral data using watershed,” in Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008.
IEEE International, vol. 3. IEEE, 2008, pp. III–652.
9-Aug-18 Nitte Meenakshi Institute of Technology 23
