Improvement of Target-Detection Algorithms Based on Adaptive Three-Dimensional Filtering

被引:38
作者
Bourennane, Salah [1 ,2 ]
Fossati, Caroline [1 ]
Cailly, Alexis [3 ]
机构
[1] Ecole Cent Marseille, Inst Fresnel CNRS 6133, GSM, F-13397 Marseille, France
[2] Inst Fresnel CNRS, UMR 6133, GSM, F-13397 Marseille, France
[3] DU St Jerome, Inst Fresnel, GSM, F-13397 Marseille, France
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2011年 / 49卷 / 04期
关键词
Adaptive 3-D filtering (ATF); hyperspectral; quadtree; target detection; tensor; HYPERSPECTRAL DATA; IMAGERY; SIGNAL; APPROXIMATION; COMPRESSION; REDUCTION; QUALITY; BAND;
D O I
10.1109/TGRS.2010.2076288
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Target detection is a key issue in processing hyperspectral images (HSIs). Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. In most cases, both the target spatial distributions and the spectral signatures are unknown, so each pixel is separately tested and appears as a target when it significantly differs from the background. In this paper, we propose two algorithms to improve the signal-to-noise ratio (SNR) of hyperspectral data, leading to detectors that are robust to noise. These algorithms consist in integrating adaptive spatial/spectral filtering into the adaptive matched filter and adaptive coherence estimator. Considering the HSIs as tensor data, our approach introduces a data representation involving multidimensional processing. It combines the advantages of spatial and spectral information using an alternating least square algorithm. To estimate the signal subspace dimension in each spatial mode, we extend the Akaike information criterion, and we develop an iterative algorithm for spectral-mode rank estimation. We demonstrate the interest of integrating the quadtree decomposition to perform an adaptive 3-D filtering and thereby preserve the local image characteristics. This leads to a significant improvement in terms of denoised tensor SNR and, consequently, in terms of detection probability. The performance of our method is exemplified using simulated and real-world HYperspectral Digital Imagery Collection Experiment images.
引用
收藏
页码:1383 / 1395
页数:13
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