Satellite Image Clustering

Remote Sensing technology senses and measures the radiation or reflectance of samples of distant objects, and allows extraction of information which includes detection and recognition of objects and its coverage. Image classification methods identify the objects represented by each pixel in the satellite image based on its spectral wavelength and time series. In this chapter, the basics of satellite image classification and its types are presented. The unsupervised classification methods such as K-means, Gaussian mixture model, self-organizing maps, and Hidden Markov models are described for clustering of satellite images.

[1]  Anthony J. Richardson,et al.  Using self-organizing maps to identify patterns in satellite imagery , 2003 .

[2]  Kishor P. Upla,et al.  Pan-sharpening: Use of difference of Gaussians , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[3]  Sanjay Ranka,et al.  An effic ient k-means clustering algorithm , 1997 .

[4]  Ujjwal Maulik,et al.  An evolutionary technique based on K-Means algorithm for optimal clustering in RN , 2002, Inf. Sci..

[5]  Francesca Bovolo,et al.  Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images , 2014 .

[6]  Lorenzo Bruzzone,et al.  An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Florence Tupin,et al.  Unsupervised classification of radar images using hidden Markov chains and hidden Markov random fields , 2003, IEEE Trans. Geosci. Remote. Sens..

[8]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[9]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Driss Aboutajdine,et al.  Textural feature selection by joint mutual information based on Gaussian mixture model for multispectral image classification , 2010, Pattern Recognit. Lett..

[11]  G. Celeux,et al.  Variable Selection for Clustering with Gaussian Mixture Models , 2009, Biometrics.

[12]  P. Chavez,et al.  Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis , 1989 .

[13]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[14]  Mourad Zribi,et al.  An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k-Means Clustering Algorithm Initialization , 2009 .

[15]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[16]  Mohamad Awad Segmentation of Satellite Images Using Self-Organizing Maps , 2010 .

[17]  T. Kohonen,et al.  Self-organizing semantic maps , 1989, Biological Cybernetics.

[18]  J. Shan,et al.  Principal Component Analysis for Hyperspectral Image Classification , 2002 .

[19]  Weiguo Liu,et al.  Comparison of non-linear mixture models: sub-pixel classification , 2005 .

[20]  Nilanjan Dey,et al.  Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images , 2017, Comput. Biol. Chem..

[21]  Eric L. Miller,et al.  Adaptive difference of Gaussians to improve subsurface imagery , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[22]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[23]  Jorma Laaksonen,et al.  Variants of self-organizing maps , 1990, International 1989 Joint Conference on Neural Networks.

[24]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

[25]  Yihua Tan,et al.  Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[26]  Quan Wang,et al.  HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm , 2012, ArXiv.

[27]  Assad H. Thary Al-Ghrairi,et al.  Satellite Image Classification Using Moment and SVD Method , 2016 .

[28]  Ashish Ghosh,et al.  Semi-supervised change detection using modified self-organizing feature map neural network , 2014, Appl. Soft Comput..

[29]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[30]  Francesca Bovolo,et al.  Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[32]  Antonio J. Plaza,et al.  A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Klaus Schulten,et al.  Self-organizing maps: ordering, convergence properties and energy functions , 1992, Biological Cybernetics.

[34]  Gabriele Moser,et al.  Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model , 2013, IEEE Geoscience and Remote Sensing Letters.

[35]  Sankar K. Pal,et al.  Segmentation of multispectral remote sensing images using active support vector machines , 2004, Pattern Recognit. Lett..

[36]  Guido Lemoine,et al.  Parcel based classification for agricultural mapping and monitoring using multi-temporal satellite image sequences , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[37]  Stefan Walk,et al.  BEYOND HAND-CRAFTED FEATURES IN REMOTE SENSING , 2013 .

[38]  Christophe Biernacki,et al.  Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models , 2003, Comput. Stat. Data Anal..

[39]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Hector Gomez,et al.  Satellite Image Classification by Self-Organized Maps on GRID Computing Infrastructures , 2009 .

[41]  H. Abdi,et al.  Principal component analysis , 2010 .

[42]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[43]  Hong Sun,et al.  Unsupervised Satellite Image Classification Using Markov Field Topic Model , 2013, IEEE Geoscience and Remote Sensing Letters.

[44]  Timo Honkela,et al.  WEBSOM - Self-organizing maps of document collections , 1998, Neurocomputing.

[45]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[46]  Tapas Ranjan Martha,et al.  Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[48]  Mark Stamp,et al.  Singular value decomposition and metamorphic detection , 2015, Journal of Computer Virology and Hacking Techniques.

[49]  Ma Jianwen,et al.  Land-use classification using ASTER data and self-organized neutral networks , 2005 .

[50]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[51]  Robert M. Gray,et al.  Image classification by a two-dimensional hidden Markov model , 2000, IEEE Trans. Signal Process..

[52]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[53]  Nilanjan Dey,et al.  Foliage area computation using Monarch Butterfly Algorithm , 2014, 2014 1st International Conference on Non Conventional Energy (ICONCE 2014).

[54]  Vahid Nourani,et al.  Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling , 2013 .

[55]  Elcio H. Shiguemori,et al.  Change Detection in Satellite Images Using Self-Organizing Maps , 2015, 2015 12th International Conference on Information Technology - New Generations.

[56]  Xuefei Hu,et al.  Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. , 2009 .

[57]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[58]  Nilanjan Dey,et al.  Theoretical Analysis of Different Classifiers under Reduction Rough Data Set: A Brief Proposal , 2016, Int. J. Rough Sets Data Anal..

[59]  T. Kohonen Analysis of a simple self-organizing process , 1982, Biological Cybernetics.

[60]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[61]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[62]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[63]  P. Sathya,et al.  Classification and Segmentation in Satellite Imagery Using Back Propagation Algorithm of ANN and K-Means Algorithm , 2011 .

[64]  Nilanjan Dey,et al.  Image Fusion Incorporating Parameter Estimation Optimized Gaussian Mixture Model and Fuzzy Weighted Evaluation System: A Case Study in Time-Series Plantar Pressure Data Set , 2017, IEEE Sensors Journal.

[65]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[66]  Eric D. Kolaczyk,et al.  Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing , 2003 .

[67]  Hong Sun,et al.  Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[68]  Jon Atli Benediktsson,et al.  Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas , 2009, EURASIP J. Adv. Signal Process..

[69]  Stefan Winkler,et al.  Ground-based image analysis: A tutorial on machine-learning techniques and applications , 2016, IEEE Geoscience and Remote Sensing Magazine.

[70]  C. Ji Land-use classification of remotely sensed data using Kohonen Self-Organizing Feature Map neural networks. , 2000 .

[71]  Nilanjan Dey,et al.  Convolutional Neural Network Based Clustering and Manifold Learning Method for Diabetic Plantar Pressure Imaging Dataset , 2017 .

[72]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[73]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[75]  Giorgos Mallinis,et al.  A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data , 2015, Remote. Sens..

[76]  Nilanjan Dey,et al.  Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification , 2019, Int. J. Mach. Learn. Cybern..

[77]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[78]  Z. Zivkovic Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[79]  Torbjørn Eltoft,et al.  Classification With a Non-Gaussian Model for PolSAR Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[80]  Kyung-Soo Han,et al.  A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data , 2004 .

[81]  Nilanjan Dey,et al.  Parallel image segmentation using multi-threading and k-means algorithm , 2013, 2013 IEEE International Conference on Computational Intelligence and Computing Research.

[82]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[83]  Guoyou Wang,et al.  Robust Scale-Invariant Feature Matching for Remote Sensing Image Registration , 2009, IEEE Geoscience and Remote Sensing Letters.

[84]  Kaare Brandt Petersen,et al.  Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods , 2013, IEEE Signal Processing Magazine.

[85]  Hichem Sahli,et al.  Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing , 2015, Remote. Sens..