Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

[1]  N. Aranki,et al.  Hyperspectral data compression , 2003 .

[2]  Lorenzo Bruzzone,et al.  A technique for feature selection in multiclass problems , 2000 .

[3]  Mario Chica-Olmo,et al.  Downscaling cokriging for image sharpening , 2006 .

[4]  G. Shaw,et al.  Signal processing for hyperspectral image exploitation , 2002, IEEE Signal Process. Mag..

[5]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Xiao‐Hai Yan,et al.  Development and application of a neural network based ocean colour algorithm in coastal waters , 2005 .

[7]  Lorenzo Bruzzone,et al.  Kernel-based methods for hyperspectral image classification , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Mihai Datcu,et al.  Wavelet-Based Despeckling of SAR Images Using Gauss–Markov Random Fields , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Antonio J. Plaza,et al.  Impact of Initialization on Design of Endmember Extraction Algorithms , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Luis Gómez-Chova,et al.  Semisupervised Image Classification With Laplacian Support Vector Machines , 2008, IEEE Geoscience and Remote Sensing Letters.

[12]  Enrico Magli,et al.  Transform Coding Techniques for Lossy Hyperspectral Data Compression , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Mario Winter,et al.  N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data , 1999, Optics & Photonics.

[14]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[15]  Heesung Kwon,et al.  A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery , 2007, EURASIP J. Adv. Signal Process..

[16]  Lorenzo Bruzzone,et al.  Kernel methods for remote sensing data analysis , 2009 .

[17]  José Luis Rojo-Álvarez,et al.  Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Julio Martín-Herrero,et al.  Anisotropic Diffusion in the Hypercube , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Thomas L. Ainsworth,et al.  Improved Manifold Coordinate Representations of Large-Scale Hyperspectral Scenes , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Stefano Pignatti,et al.  Experimental Approach to the Selection of the Components in the Minimum Noise Fraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Philippe Bolon,et al.  Statistical and operational performance assessment of multitemporal SAR image filtering , 2003, IEEE Trans. Geosci. Remote. Sens..

[22]  Shen-En Qian,et al.  Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Masoud Nikravesh,et al.  Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing) , 2006 .

[24]  Gustavo Camps-Valls,et al.  Semi-Supervised Graph-Based Hyperspectral Image Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[25]  William A. Pearlman,et al.  Three-Dimensional Wavelet-Based Compression of Hyperspectral Images , 2006, Hyperspectral Data Compression.

[26]  Fabio Del Frate,et al.  Use of Neural Networks for Automatic Classification From High-Resolution Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[27]  José Luis Rojo-Álvarez,et al.  Robust support vector regression for biophysical variable estimation from remotely sensed images , 2006, IEEE Geoscience and Remote Sensing Letters.

[28]  Rick Archibald,et al.  Feature Selection and Classification of Hyperspectral Images With Support Vector Machines , 2007, IEEE Geoscience and Remote Sensing Letters.

[29]  Lorenzo Bruzzone,et al.  A Novel Transductive SVM for Semisupervised Classification of Remote-Sensing Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Francesca Bovolo,et al.  A Multilevel Parcel-Based Approach to Change Detection in Very High Resolution Multitemporal Images , 2005, IEEE Geoscience and Remote Sensing Letters.

[31]  Gustavo Camps-Valls,et al.  Semisupervised Remote Sensing Image Classification With Cluster Kernels , 2009, IEEE Geoscience and Remote Sensing Letters.

[32]  Torbjørn Eltoft,et al.  Homomorphic wavelet-based statistical despeckling of SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Masoud Nikravesh,et al.  Feature Extraction - Foundations and Applications , 2006, Feature Extraction.

[34]  Harold Mott,et al.  Remote Sensing with Polarimetric Radar , 2007 .

[35]  Grégoire Mercier,et al.  Partially Supervised Oil-Slick Detection by SAR Imagery Using Kernel Expansion , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Francesca Bovolo,et al.  A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Xavier Otazu,et al.  Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[39]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[40]  Julien Radoux,et al.  Bayesian Data Fusion for Adaptable Image Pansharpening , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Jordi Inglada,et al.  Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features , 2007 .

[42]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[43]  Jordi Inglada,et al.  Analysis of Artifacts in Subpixel Remote Sensing Image Registration , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[44]  Mahesh Pal,et al.  Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data , 2006 .