Abstract
Dictionary pruning step is often employed prior to the sparse unmixing process to improve the performance of library aided unmixing. This paper presents a novel recursive PCA approach for dictionary pruning of linearly mixed hyperspectral data motivated by the low-rank structure of a linearly mixed hyperspectral image. Further, we propose a mutual coherence reduction method for pre-unmixing to enhance the performance of pruning. In the pruning step we, identify the actual image endmembers utilizing the low-rank constraint. We obtain an augmented version of the data by appending each image endmember and compute PCA reconstruction error, which is a convex surrogate of matrix rank. We identify the pruned library elements according to PCA reconstruction error ratio (PRER) and PCA reconstruction error difference (PRED) and employ a recursive formulation for repeated PCA computation. Our proposed formulation identifies the exact endmember set at an affordable computational requirement. Extensive simulated and real image experiments exhibit the efficacy of the proposed algorithm in terms of its accuracy, computational complexity and noise performance.
Highlights
Hyperspectral imaging has attained immense popularity in remote sensing community in recent years owing to its high accuracy in classification and objbfect identification from remotely sensed images
We evaluate the performance of the unmixing methods on two parameters signal to reconstruction error (SRE) and the probability of detection (Pr Det)
This paper introduces Principal component analysis (PCA) as an alternative dictionary pruning method, which accurately estimates the exact spectral library endmember set if the noise level is under certain limit and the number of endmembers present in the image is accurately estimated
Summary
Hyperspectral imaging has attained immense popularity in remote sensing community in recent years owing to its high accuracy in classification and objbfect identification from remotely sensed images. Diverse application such as environmental studies [1], agricultural studies [2,3], mineral mapping [4], surveillance employ remotely sensed hyperspectral images. The object identification essentially employs the spectral unmixing method, which essentially estimates the reflectance profile of the spectrally distinct materials or endmembers. Spectral unmixing methods necessarily estimate the reflectance pattern of endmembers present in the image and compute its fractional abundance. Traditional unmixing methods involve three stages estimation of the number of endmembers, endmember estimation and calculation of abundance of endmembers [7]
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