Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images

Hsuan Ren Chein-I Chang, MEMBER SPIE Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, Maryland 21250 Abstract. Due to significantly improved spatial and spectral resolution, hyperspectral sensors can now detect many substances that cannot be resolved by multispectral sensors. However, this comes at the price that many unknown and unidentified signal sources, referred to as interferers, may also be extracted unexpectedly. Such interferers generally produce additional noise effects on target detection and must therefore be taken into account. The problem associated with this interference is challenging because its nature is generally unknown and it cannot be identified from an image scene. This paper presents an approach, called the target-constrained interference-minimized filter (TCIMF), which does not require one to identify interferers, but can minimize the effects caused by interference. It designs a finite-impulse-response filter that specifies targets of interest in such a way that the desired targets and undesired targets will be passed through and rejected by the filter, respectively; the filter output energy resulting from unknown signal sources is also minimized. More precisely, the TCIMF accomplishes three tasks simultaneously: detection of the desired targets, elimination of the undesired targets, and minimization of interfering effects. A recently developed technique, constrained energy minimization (CEM), can be considered as a suboptimal version of the TCIMF. Computer simulations and hyperspectral image experiments are conducted to demonstrate advantages of the TCIMF over the CEM. © 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)02912-3]

[1]  E. Ashton,et al.  Algorithms for the Detection of Su b-Pixel Targets in Multispectral Imagery , 1998 .

[2]  Edward J. Wegman,et al.  Statistical Signal Processing , 1985 .

[3]  J. Boardman.  Inversion Of Imaging Spectrometry Data Using Singular Value Decomposition , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[4]  D. Roberts,et al.  A new approach to quantifying abundances of materials in multispectral images , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Mark L. G. Althouse,et al.  Least squares subspace projection approach to mixed pixel classification for hyperspectral images , 1998, IEEE Trans. Geosci. Remote. Sens..

[6]  J. Boardman,et al.  Leveraging the High Dimensionality of AVIRIS Data for improved Sub-Pixel Target i Unmixing and Rejection of False Positives : Mixture Tuned Matched Filtering , 1998 .

[7]  Qian Du,et al.  A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[8]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[9]  Chein-I Chang,et al.  A generalized orthogonal subspace projection approach to unsupervised multispectral image classification , 2000, IEEE Trans. Geosci. Remote. Sens..

[10]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[11]  Chein-I Chang,et al.  Kalman filtering approach to multispectral/hyperspectral image classification , 1999 .

[12]  John B. Adams,et al.  Quantitative subpixel spectral detection of targets in multispectral images. [terrestrial and planetary surfaces] , 1992 .

[13]  O. L. Frost,et al.  An algorithm for linearly constrained adaptive array processing , 1972 .

[14]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[15]  W. Farrand.  Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique , 1997 .

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

[17]  C. Pieters,et al.  Remote geochemical analysis : elemental and mineralogical composition , 1993 .

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

[19]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

[20]  Qian Du,et al.  Interference and noise-adjusted principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[21]  John B. Adams,et al.  SPECTRAL MIXTURE ANALYSIS - NEW STRATEGIES FOR THE ANALYSIS OF MULTISPECTRAL DATA , 1994 .

[22]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[23]  B.D. Van Veen,et al.  Beamforming: a versatile approach to spatial filtering , 1988, IEEE ASSP Magazine.

[24]  Chein-I Chang,et al.  Linearly constrained minimum variance beamforming approach to target detection and classification for hyperspectral imagery , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[25]  Chein-I Chang,et al.  An unsupervised vector quantization-based target subspace projection approach to mixed pixel detection and classification in unknown background for remotely sensed imagery , 1999, Pattern Recognit..

[26]  John B. Adams,et al.  Simple Models For Complex Natural Surfaces: A Strategy For The Hyperspectral Era Of Remote Sensing , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[27]  Chein-I Chang,et al.  Unsupervised interference rejection approach to target detection and classification for hyperspectral imagery , 1998 .

[28]  Chein-I Chang,et al.  Constrained subpixel target detection for remotely sensed imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[29]  Chein-I Chang,et al.  An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis , 2000, IEEE Trans. Inf. Theory.

[30]  A. Goetz,et al.  Terrestrial imaging spectroscopy , 1988 .