Advanced Hyperspectral Remote Sensing for Target Detection

Hyperspectral sensors provide 3-D images with high spatial and spectral resolution. Acquired data can be utilized in diverse applications such as detection and control of hazardous agents in atmosphere and water, military targets, and so on. Over the last decade, hyperspectral remote sensing algorithms for target detection have evolved from the spectral-based methods, which only use spectral information, to more recent methods based on spatial-spectral information. Spatial information plays a crucial role to improve the efficiency of the algorithms. Furthermore, the parallelization of the algorithms reduces the computation time. Developments in the area of commodity computing provide affordable approach for target detection applications with real-time constraint. We will give a scientific overview of recent target detection algorithms which try to overcome existing limitations (e.g. spectral variability or background interference) in hyperspectral remote sensing. Unlike current target detection methods in literature, this study explains and assesses different aspects of developments in target detection algorithms comprehensively. In particular, this study focuses on development in atmospheric correction methods which especially deal with background interference, development in methods based on spectral information and spectral-spatial information (both methods especially deal with spectral variability), and parallelization of the algorithms. With consideration of hyperspectral data challenges in real-world, an optimum approach is the adaptive algorithm based on spatial-spectral information in which their computation is performed in parallel

[1]  Chein-I. Chang,et al.  Linear Spectral Mixture Analysis Based Approaches to Estimation of Virtual Dimensionality in Hyperspectral Imagery , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Chein-I Chang,et al.  GPU implementation of fully constrained linear spectral unmixing for remotely sensed hyperspectral data exploitation , 2010, Optical Engineering + Applications.

[3]  Antonio J. Plaza,et al.  Parallel Implementation of Target and Anomaly Detection Algorithms for Hyperspectral Imagery , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Gary A. Shaw,et al.  Hyperspectral Image Processing for Automatic Target Detection Applications , 2003 .

[5]  Antonio J. Plaza,et al.  Spatial-spectral endmember extraction from remotely sensed hyperspectral images using the watershed transformation , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[6]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[7]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[8]  J. Boardman,et al.  Mapping target signatures via partial unmixing of AVIRIS data: in Summaries , 1995 .

[9]  Sandor Imre,et al.  Recent Developments and Future Directions , 2013 .

[10]  Marcos J. Montes,et al.  An Atmospheric Correction Algorithm for Remote Sensing of Bright Coastal Waters Using MODIS Land and Ocean Channels in the Solar Spectral Region , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Antonio J. Plaza,et al.  Parallel implementation of endmember extraction algorithms using NVidia graphical processing units , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[12]  S. J. Young,et al.  An in‐scene method for atmospheric compensation of thermal hyperspectral data , 2002 .

[13]  A. Berk.  MODTRAN : A moderate resolution model for LOWTRAN7 , 1989 .

[14]  Antonio J. Plaza,et al.  Spectral-textural endmember extraction , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[15]  Stefania Matteoli,et al.  Forward Modeling and Atmospheric Compensation in hyperspectral data: Experimental analysis from a target detection perspective , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[16]  Antonio J. Plaza,et al.  Improving the Performance of Hyperspectral Image and Signal Processing Algorithms Using Parallel, Distributed and Specialized Hardware-Based Systems , 2010, J. Signal Process. Syst..

[17]  Antonio J. Plaza,et al.  FPGA Implementation of the Pixel Purity Index Algorithm for Remotely Sensed Hyperspectral Image Analysis , 2010, EURASIP J. Adv. Signal Process..

[18]  Wallace M. Porter,et al.  The airborne visible/infrared imaging spectrometer (AVIRIS) , 1993 .

[19]  Antonio J. Plaza,et al.  Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images , 2010, EURASIP J. Adv. Signal Process..

[20]  Xiaoli Yu,et al.  Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution , 1990, IEEE Trans. Acoust. Speech Signal Process..

[21]  Marcos J. Montes,et al.  A multi-channel atmospheric correction algorithm for remote sensing of coastal waters , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[22]  Antonio J. Plaza,et al.  Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Antonio J. Plaza,et al.  Cluster versus GPU implementation of an Orthogonal Target Detection Algorithm for Remotely Sensed Hyperspectral Images , 2010, 2010 IEEE International Conference on Cluster Computing.

[24]  Anne B. Kahle,et al.  The TIMS Data User's Workshop , 1986 .

[25]  A. Gillespie,et al.  Lithologic mapping of silicate rocks using TIMS , 1986 .

[26]  D. Roberts,et al.  Estimation of aerosol optical depth and additional atmospheric parameters for the calculation of apparent reflectance from radiance measured by the Airborne Visible/Infrared Imaging Spectrometer , 1993 .

[27]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Dimitris G. Manolakis,et al.  Hyperspectral detection algorithms: use covariances or subspaces? , 2009, Optical Engineering + Applications.

[29]  José M. Bioucas-Dias,et al.  Estimation of signal subspace on hyperspectral data , 2005, SPIE Remote Sensing.

[30]  Antonio J. Plaza,et al.  On the use of spectral libraries to perform sparse unmixing of hyperspectral data , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[31]  Chein-I Chang,et al.  Automatic spectral target recognition in hyperspectral imagery , 2003 .

[32]  Antonio Plaza,et al.  Optimization of a Hyperspectral Image Processing Chain Using Heterogeneous and GPU-Based Parallel Computing Architectures , 2009 .

[33]  R. Green,et al.  AIS-2 radiometry and a comparison of methods for the recovery of ground reflectance , 1987 .

[34]  Bo-Cai Gao,et al.  A Review of Atmospheric Correction Techniques for Hyperspectral Remote Sensing of Land Surfaces and Ocean Color , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[35]  Thomas W. Cooley,et al.  Effects of signature mismatch on hyperspectral detection algorithms , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[36]  Jian Li,et al.  On robust Capon beamforming and diagonal loading , 2003, IEEE Trans. Signal Process..

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

[38]  Chein-I. Chang.  Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[39]  D. C. Robertson,et al.  MODTRAN: A Moderate Resolution Model for LOWTRAN , 1987 .

[40]  E. M. Winter,et al.  Anomaly detection from hyperspectral imagery , 2002, IEEE Signal Process. Mag..

[41]  Ferran Gascon,et al.  Earth-Atmosphere radiative transfer in DART model , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[42]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[43]  Antonio J. Plaza,et al.  FPGA for Computing the Pixel Purity Index Algorithm on Hyperspectral Images , 2010, ERSA.

[44]  Gail P. Anderson,et al.  Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data , 2002, Applied Imagery Pattern Recognition Workshop, 2002. Proceedings..

[45]  Glenn Healey,et al.  Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions , 1999, IEEE Trans. Geosci. Remote. Sens..

[46]  Antonio J. Plaza,et al.  Parallel implementation of the N-FINDR endmember extraction algorithm on commodity graphics processing units , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[47]  Antonio J. Plaza,et al.  Clusters Versus FPGA for Parallel Processing of Hyperspectral Imagery , 2008, Int. J. High Perform. Comput. Appl..

[48]  Francisco Tirado,et al.  GPU for Parallel On-Board Hyperspectral Image Processing , 2008, Int. J. High Perform. Comput. Appl..

[49]  Antonio J. Plaza,et al.  Spatial-spectral preprocessing for volume-based endmember extraction algorithms using unsupervised clustering , 2010, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

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

[51]  Antonio J. Plaza,et al.  Parallel Morphological Endmember Extraction Using Commodity Graphics Hardware , 2007, IEEE Geoscience and Remote Sensing Letters.

[52]  Robert O. Green,et al.  Calibration to surface reflectance of terrestrial imaging spectrometry data: Comparison of methods , 1995 .

[53]  Antonio Plaza,et al.  GPU implementation of the pixel purity index algorithm for hyperspectral image analysis , 2010, 2010 IEEE International Conference On Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS).

[54]  Stefania Matteoli,et al.  Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Antonio J. Plaza,et al.  Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images , 2010, 2010 20th International Conference on Pattern Recognition.

[56]  J. Boardman,et al.  Geometric mixture analysis of imaging spectrometry data , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[57]  Roger N. Clark,et al.  Causes of spurious features in spectral reflectance data , 1987 .

[58]  Abel Paz,et al.  GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis , 2010, Optical Engineering + Applications.

[59]  A.J. Plaza,et al.  Recent developments and future directions in parallel processing of remotely sensed hyperspectral images , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.