Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection

Abstract

Compressive sensing (CS) has received considerable interest in hyperspectral sensing. Recent articles have also exploited the benefits of CS in hyperspectral image classification (HSIC) in the compressively sensed band domain (CSBD). However, on many occasions, the requirement of full bands is not necessary for HSIC to perform well. So, a great challenge arises in determining the minimum number of compressively sensed bands (CSBs), n CSB , needed to achieve full-band performance. Practically, the value of n CSB varies with the complexity of an imaged scene. Although virtual dimensionality (VD) has been used to estimate the number of bands to be selected, n BS , it is not applicable to CSBD because a CSB is actually a mixture of n CSB bands sensed by a random sensing matrix, while VD is used to estimate n BS which is the number of single bands to be selected. As expected, n CSB will be generally smaller than n BS . To estimate an optimal value of n CSB , two feature selection approaches, filter and wrapper methods, are proposed to extract scene features that can be used to estimate the minimum value of n CSB required to maximize performance with minimum redundancy. Specifically, these methods are fully automated by leveraging optimal partitioning schemes which enable classification to further reduce storage requirements in CSBD. Finally, a set of experiments are conducted using real-world hyperspectral images to demonstrate the viability of the proposed approach.