Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

Abstract

Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and are sometimes ineffective for applications that require timeliness, such as disaster prevention or target detection. This paper proposes an online BS method that allows us obtain instant BS results in a progressive manner during HSI data transmission, which is carried out under band-interleaved-by-sample/pixel (BIS/BIP) format. Such a revolutionary method is called progressive sample processing of band selection (PSP-BS). In PSP-BS, BS can be done recursively pixel by pixel, so that the instantaneous BS can be achieved without waiting for all the pixels of an image. To develop a PSP-BS algorithm, we proposed PSP-OMPBS, which adopted the recursive version of a self-sparse regression BS method (OMPBS) as a native algorithm. The experiments conducted on two real hyperspectral images demonstrate that PSP-OMPBS can progressively output the BS with extremely low computing time. In addition, the convergence of BS results during transmission can be further accelerated by using a pre-defined pixel transmission sequence. Such a significant advantage not only allows BS to be done in a real-time manner for the future satellite data downlink, but also determines the BS results in advance, without waiting to receive every pixel of an image.

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

“…The number of divided blocks K deciding the dimension of extracted feature is chosen from the set of {3, 5,10,15,20,25,30,35,40, 50} in sequence. Figure 10 presents the experimental result.…”

“…These spectra are represented by hundreds of continuous bands that can meticulously describe the characteristics of different materials to recognize their subtle differences [3]. Therefore, owing to this good discriminative property of hyperspectral image, it has been widely used in many remote sensing research fields [4,5], such as image denoising [6,7], hyperspectral unmixing [8,9], band selection [10,11], target detection [12,13], and image classification [14,15]. They all have important practical applications in geological exploration, urban remote sensing and planning management, environment and disaster monitoring, precision agriculture, archaeology, etc.…”

Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

“…So an alternatively iterative algorithm is used to find the optimal solution in this work. When A c is fixed, W c is calculated by Equation (9). Then fixing W c and solving Equation (8), A c can be obtained easily as follows.…”

“…For LRX method, in order to avoid covariance matrix singular problem, we finally use four pairs of window sizes (outside, inner) including (17,7), (17,9), (19,7), and (19,9) by comprehensively considering all the spectral dimensions of all the hyperspectral images. As for SRD and CRD, we define six kinds of different sizes including (13,7), (15,7), (17,7), (17,9), (19,7), and (19,9). In addition, the regularized parameter λ involved in CRD method is set as 10 −6 referring to its original work [45].…”

“…These spectra are divided into hundreds of approximately continuous and very narrow spectral bands which have a strong ability to precisely characterize different objects and accurately recognize the subtle differences between surface materials [2,3]. Benefiting from its high spectral resolution, hyperspectral image has been successfully used in many applications [4], such as target detection [5,6], image classification [7,8], band selection [9,10], and hyperspectral unmixing [11,12].…”