Progressive Compressively Sensed Band Processing for Hyperspectral Classification

Abstract

Compressive sensing (CS) has recently been demonstrated as an enabling technology for hyperspectral sensing on remote and autonomous platforms. The power, on-board storage, and computation requirements associated with the high dimensionality of hyperspectral images (HSI) are still limiting factors for many applications. A recent work has exploited the benefits of CS to perform HSI classification directly in the compressively sensed band domain (CSBD). Since the required number of compressively sensed bands (CSBs) needed to achieve full band performance varies with the complexity of an image scene, this article presents a progressive band processing (PBP) approach, called progressive CSB classification (PCSBC), to adaptively determine an appropriate number of CSBs required to achieve full band performance, while also providing immediate feedback from progressions of class classification predictions carried out by PCSBC. By taking advantage of PBP, new progression metrics and stopping criteria are also designed for PCSBC. Four real-world HSIs are used to demonstrate the utility of PCSBC.