Abstract
In recent years, representation-based methods have attracted more attention in the hyperspectral image (HSI) classification. Among them, sparse representation-based classifier (SRC) and collaborative representation-based classifier (CRC) are the two representative methods. However, SRC only focuses on sparsity but ignores the data correlation information. While CRC encourages grouping correlated variables together but lacks the ability of variable selection. As a result, SRC and CRC are incapable of producing satisfied performance. To address these issues, in this work, a correlation adaptive representation (CAR) is proposed, enabling a CAR-based classifier (CARC). Specifically, the proposed CARC is able to explore sparsity and data correlation information jointly, generating a novel representation model that is adaptive to the structure of the dictionary. To further exploit the correlation between the test samples and the training samples effectively, a distance-weighted Tikhonov regularization is integrated into the proposed CARC. Furthermore, to handle the small training sample problem in the HSI classification, a multi-feature correlation adaptive representation-based classifier (MFCARC) and MFCARC with Tikhonov regularization (MFCART) are presented to improve the classification performance by exploring the complementary information across multiple features. The experimental results show the superiority of the proposed methods over state-of-the-art algorithms.
Highlights
Hyperspectral images (HSIs) provide valuable spectral information by using the hyperspectral imaging sensors to capture data at hundreds of narrow contiguous bands from the same spatial location
State-of-the-art classification algorithms are used as the benchmark, including kernel sparse representation classification (KSRC) [54], multiscale adaptive sparse representation classification (MASR) [55], collaborative representation classification with Tikhonov regularization (CRT) [19], kernel fused representation classification via the composite kernel with ideal regularization (KFRC-CKIR) [27], multiple feature sparse representation classification (MF-sparse representation-based classifier (SRC)) [34], multiple feature joint sparse representation classification (MF-joint SRC (JSRC)) [34], and multi-feature based adaptive sparse representation classification (MFASR) [36]
By taking the correlation between the test sample and the training samples into consideration, correlation adaptive representation with Tikhonov regularization (CART) is developed to make the representation model reveal the true geometry of feature space
Summary
Hyperspectral images (HSIs) provide valuable spectral information by using the hyperspectral imaging sensors to capture data at hundreds of narrow contiguous bands from the same spatial location. In the past few years, HSIs have been extensively applied in various fields [1,2,3,4,5]. HSI classification is a crucial task for a wide variety of real-world applications, such as ecological science, mineralogy, and precision agriculture [6]. There exist open challenges in the HSI classification task. The hundreds of spectral bands provide sufficient information, it results in the Hughes phenomenon. Given that the practical sample labeling is difficult and expensive, lacking sufficient labeled samples is another major challenge in HSI classification techniques
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