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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 interfer-ers, may also be extracted unexpectedly. Such interferers generally produce(More)
—Orthogonal subspace projection (OSP) has been successfully applied in hyperspectral image processing. In order for the OSP to be effective, the number of bands must be no less than that of signatures to be classified. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. Such(More)
—Over the past years, many algorithms have been developed for multispectral and hyperspectral image classification. A general approach to mixed pixel classification is linear spectral unmixing, which uses a linear mixture model to estimate the abundance fractions of signatures within a mixed pixel. As a result, the images generated for classification are(More)
—In this letter, we conduct a comparative study and investigate the relationship between two well-known techniques in hyperspectral image detection and classification: orthogonal subspace projection (OSP) and constrained energy minimization (CEM). It is shown that they are closely related and essentially equivalent provided that the noise is white with(More)
The spectral angle mapper (SAM) has been widely used in multispectral and hyperspectral image analysis to measure spectral similarity between substance signatures for material identification. It has been shown that the SAM is essentially the Euclidean distance when the spectral angle is small. Most recently, a stochastic measure, called the spectral(More)
In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classiÿcation as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that(More)
—In this paper, we present a linearly constrained minimum variance (LCMV) beamforming approach to real time processing algorithms for target detection and classification in hyperspectral imagery. The only required knowledge for these LCMV-based algorithms is targets of interest. The idea is to design a finite impulse response (FIR) filter to pass through(More)
—One of the challenges in remote sensing image processing is subpixel detection where the target size is smaller than the ground sampling distance, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected spectrally at the subpixel level, not spatially as ordinarily conducted by classical image processing(More)
Subpixel detection in multispectral imagery presents a challenging problem due to relatively low spatial and spectral resolution. We present a generalized constrained energy minimization (GCEM) approach to detecting targets in multispectral imagery at subpixel level. GCEM is a hybrid technique that combines a constrained energy minimization (CEM) method(More)