Proc. SPIE. 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
KEYWORDS: Target detection, Hyperspectral imaging, Statistical analysis, 3D acquisition, Detection and tracking algorithms, Data modeling, 3D modeling, Feature extraction, Environmental sensing, 3D image processing
The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The use of subspace models for targets and backgrounds allows detection that is invariant to changing environmental conditions. The non-Gaussian behavior of target and background distribution residuals complicates the development of subspace-based detection methods. In this paper, we use discriminant analysis for feature extraction for separating subpixel 3D objects from cluttered backgrounds. The nonparametric estimation of distributions is used to establish the statistical models using the length and direction of residuals. Candidate subspaces are then evaluated to maximize their discriminatory power which is measured between estimated distributions of targets and backgrounds. In this context, a likelihood ratio test is used based on background and mixed statistics for subpixel detection. The detection algorithm is evaluated for HYDICE images and a number of images simulated using DIRSIG under a variety of conditions. The experimental results demonstrate accurate detection performance on these data sets.
Object detection in hyperspectral imagery benefits from the large amount of spectral information. The effective use of this information is crucial for a detection algorithm to achieve high accuracy under challenging conditions. In this paper, we establish subspace representations for 3D objects and backgrounds to improve discriminability for 3D detection invariant to unknown illumination and atmospheric conditions. Residual variance information is utilized to generate background and mixed residual statistics which improve the separation of target and background for detection. A new detection algorithm that uses these statistics in conjunction with a likelihood ratio test is proposed for the subpixel detection of
complex 3D objects in cluttered backgrounds. Other existing algorithms, e.g. the generalized likelihood ratio test (GLRT), can be derived from this algorithm by introducing the appropriate assumptions. The new detection algorithm is evaluated for a number of images simulated using DIRSIG and also compared with other detection algorithms. The experimental results demonstrate accurate performance on these data sets.
The accuracy of subpixel detection in hyperspectral imagery degrades with approximation error arising from cluttered backgrounds and complex target objects. In this paper, we develop a non-parametric generalized likelihood ratio (NGLR) statistic for the subpixel detection of 3-D objects that is invariant to the illumination and atmospheric conditions. We construct the target and background subspaces from target models and the image data. The NGLR is established by estimating the conditional probability densities for the background and target hypotheses using subspace residuals. We use DIRSIG to evaluate the performance of NGLR for detecting subpixel 3-D objects composed of multiple materials in varying illumination and atmospheric conditions. NGLR provides accurate detection results that are invariant to the environmental conditions.
We use DIRSIG to evaluate algorithms for recognizing 3D objects defined by faces of different orientations and different materials. The experiments consider varying object pose as well as variable environmental conditions. Objects are represented using subspaces defined for the 0.4-2.5 micron spectral range. Spatial resolutions are considered that provide mixtures of multiple object surfaces and background. For recognizing 3-D objects in cluttered backgrounds, the orthogonal projection ratio (OPR) is proposed to minimize the effects of noise and approximation error. The experiments consider varying object pose as well as variable environmental conditions. Background clutter is represented using spectral subspaces that are estimated from the image data. The experiments consider the recognition of several 3D objects with various geometries and surface materials. Both desert and urban scenes are considered as well as a range of ground spatial distances.