A hyperspectral video contains frames with numerous spectral bands, providing fine reflectance information for object identification and tracking. Enriched features can be learned from spectral-spatial data using deep learning models. However, due to the difficulty in hyperspectral video collection, deep model training is often insufficient, causing reduced performance during the testing stage. To address this issue, we present a novel Band Attention Grouping-based Siamese framework (SiamBAG) for hyperspectral object tracking. SiamBAG employs massive color object tracking data to train a deep neural network. Band weights obtained by band attention module are used to group a hyperspectral image into multiple three-channel false-color images with approximate total group weights. Then multiple enhanced images obtained by histogram equalization are fed to the proposed SiamBAG network to generate a classification branch, a regression branch and a scale tuning branch. In the classification branch, the response maps of multiple groups are fused by regularized group weights to estimate the position of objects. Then the regression branch is used to obtain the initial object position of objects. The position offsets are fed back to the scale tune branch to relocate and fine-tune the object position by exploiting the similarity between template features and detection features. Experimental results demonstrate that the proposed tracker achieves superior tracking performance than other methods. The source codes of this paper will be released at https://github.com/zephyrhours/Hyperspectral-Object-Tracking-SiamBAG.

With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

Hyperspectral video camera captures spatial, spectral and temporal information of moving objects. Traditional object tracking methods developed for color videos have been applied to hyperspectral videos after compressing hundreds of spectral bands into three, which does not fully utilize the wealth spectral information. In order to address this issue, we present a tensor sparse correlation filter with a spatial-spectral weighted regularizer for object tracking. First, tensor processing is employed to reduce the spectral differences of homogeneous background, thereby producing robust spectral structure features. Second, a spatial-spectral weighted regularizer is designed in the correlation filter framework to penalize filter template by suppressing spectral features dissimilar to the center pixel in tracking. Third, a sparse constraint term and tracking context information are incorporated to suppress unexpected peaks in the response map. Finally, a reformulated stacked HOG feature extractor and a two-dimensional adaptive scale search strategy are developed to further improve the tracker’s feature discrimination and scale adaptation capability. Experimental results demonstrate that the proposed method achieves superior tracking performance than traditional correlation filter-based trackers.

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts.

With the increasing of hyperspectral datasets, multi-temporal hyperspectral change detection has gradually attracted re-searcher's attention. Most of traditional change detection methods only consider spectral information, but ignore importance of spatial structure information, which leads to low detection accuracy. In this work, a novel patch tensor-based change detection method (PTCD) is proposed for hyperspectral imagery to make full use of spatial structure information. Firstly, the tensor decomposition and reconstruction strategies are used to eliminate influence of various factors in multi-temporal dataset. Meanwhile, patch-based strategy is adopted to incorporate the non-overlapping local similar property into the proposed method to exploit spatial structural information. Finally, a specially designed detector is adopted to further improve the detection accuracy. Experiments conducted on two real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

Collaborative Representation Detection (CRD) is a very effective anomaly detection method, which is directly based on the concept that pixel under test (PUT) can be approximately linear represented by its spatial adjacent background pixels. If the adjacent background pixels are contaminated, the approximate value of PUT linearly represented by the surrounding pixels is inaccurate. In this work, an improved method for anomaly detection in hyperspectral imagery is proposed based on CRD. In our proposed method, the least squares technique first is adopted to obtain the preliminary linear representation coefficient, which is positively correlated with its contribution to PUT. Then, the purified background pixels are obtained according to the numerical value of the representation coefficient. Generally, the anomaly pixels are usually different from the background pixels, so saliency weight is imposed on the test pixel to make full use of the spatial information of inner window pixels around the test pixel. Extensive experiments for real hyperspectral datasets show that the proposed method outperforms the CRD method and other traditional detection methods.