High spectral resolution of hyperspectral images allows the detection and classification of materials in the observed images. However, existing research on hyperspectral detection mainly focuses on pixel-level study, partially due to the low spatial resolution in typical earth observation applications. With the development of imaging techniques, high-spatial-resolution hyperspectral data can be obtained and object-level detection is necessary for many applications. In this work, the object-based hyperspectral detection problem is formulated, and a convolutional neural network is then designed based on the specific characteristics of this problem. Moreover, a hyperspectral dataset with over 400 high-quality images for object-level target detection is created. Experimental results validate the proposed framework and show its superior performance.

Object Detection in Hyperspectral Images

In recent years, deep-learning-based hyperspectral image (HSI) classification networks have become one of the most dominant implementations in HSI classification tasks. Among these networks, convolutional neural networks (CNNs) and attention-based networks have prevailed over other HSI classification networks. While convolutional neural networks with perceptual fields can effectively extract local features in the spatial dimension of HSI, they are poor at capturing the global and sequential features of spectral–spatial information; networks based on attention mechanisms, for example, Transformer, usually have better ability to capture global features, but are relatively weak in discriminating local features. This paper proposes a fusion network of convolution and Transformer for HSI classification, known as FusionNet, in which convolution and Transformer are fused in both serial and parallel mechanisms to achieve the full utilization of HSI features. Experimental results demonstrate that the proposed network has superior classification results compared to previous similar networks, and performs relatively well even on a small amount of training data.

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FusionNet: A Convolution-Transformer Fusion Network for Hyperspectral Image Classification

Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

The low-rank and sparse decomposition model has been favored by the majority of hyperspectral image anomaly detection personnel, especially the robust principal component analysis(RPCA) model, over recent years. However, in the RPCA model, ℓ0 operator minimization is an NP-hard problem, which is applicable in both low-rank and sparse items. A general approach is to relax the ℓ0 operator to ℓ1-norm in the traditional RPCA model, so as to approximately transform it to the convex optimization field. However, the solution obtained by convex optimization approximation often brings the problem of excessive punishment and inaccuracy. On this basis, we propose a non-convex regularized approximation model based on low-rank and sparse matrix decomposition (LRSNCR), which is closer to the original problem than RPCA. The WNNM and Capped ℓ2,1-norm are used to replace the low-rank item and sparse item of the matrix, respectively. Based on the proposed model, an effective optimization algorithm is then given. Finally, the experimental results on four real hyperspectral image datasets show that the proposed LRSNCR has better detection performance.

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Hyperspectral Anomaly Detection Based on Improved RPCA with Non-Convex Regularization

Recent years have witnessed the flourishing of deep learning-based methods in hyperspectral anomaly detection (HAD). However, the lack of available supervision information persists throughout. In addition, existing unsupervised learning/semisupervised learning methods to detect anomalies utilizing reconstruction errors not only generate backgrounds but also reconstruct anomalies to some extent, complicating the identification of anomalies in the original hyperspectral image (HSI). In order to train a network able to reconstruct only background pixels (instead of anomalous pixels), in this article, we propose a new blind-spot self-supervised learning network (called BS3LNet) that generates training patch pairs with blind spots from a single HSI and trains the network in self-supervised fashion. The BS3LNet tends to generate high reconstruction errors for anomalous pixels and low reconstruction errors for background pixels due to the fact that it adopts a blind-spot architecture, i.e., the receptive field of each pixel excludes the pixel itself and the network reconstructs each pixel using its neighbors. The above characterization suits the HAD task well, considering the fact that spectral signatures of anomalous targets are significantly different from those of neighboring pixels. Our network can be considered a superb background generator, which effectively enhances the semantic feature representation of the background distribution and weakens the feature expression for anomalies. Meanwhile, the differences between the original HSI and the background reconstructed by our network are used to measure the degree of the anomaly of each pixel so that anomalous pixels can be effectively separated from the background. Extensive experiments on two synthetic and three real datasets reveal that our BS3LNet is competitive with regard to other state-of-the-art approaches.

BS3LNet: A New Blind-Spot Self-Supervised Learning Network for Hyperspectral Anomaly Detection

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving l1-norm minimization. However, l1-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the lp penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with lp-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for lp-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about 10% to 30% than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with l1-norm, and combined sparse and collaborative representation (CSCR).

Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

Hyperspectral target detection in complex backgrounds is a challenging and important research topic in the remote sensing field. Traditional target detectors consider the background spectrum to obey a Gaussian distribution. However, this distribution may not meet the requirements in real hyperspectral images. In addition, the background and spatial information of most existing target detection algorithms are rarely fully utilized. Therefore, a new weighted Cauchy distance Graph and local adaptive Collaborative Representation Detection (CGCRD) is proposed. First, a weighted Cauchy distance graph similarity measure is designed. In order to adjust the effect of target pixels on the graph model, a weighted Cauchy distance Laplace matrix is constructed, and then the matrix is applied to the matched filter detector. Secondly, local adaptive collaborative representation strategy is developed. The penalty coefficient is weighted by the local spatial Euclidean distance combined with the Pearson correlation coefficient, and then the detection result is obtained based on the residual. Finally, aforementioned two strategies are fused to fully utilize the spatial and spectral information. A 176-band hyperspectral image (BIT-HSI-I) dataset is collected for the target detection task. The related algorithms are performed on the BIT-HSI-I dataset, and the detection results demonstrate that the proposed algorithm has better detection performance than other state-of-the-art algorithms.

Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation

Hyperspectral target detection based on transform domain adaptive constrained energy minimization

Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.

Xiaobin Zhao

Papers

Adaptive Iterated Shrinkage Thresholding-Based Lp-Norm Sparse Representation for Hyperspectral Imagery Target Detection

Hyperspectral Target Detection Based on Weighted Cauchy Distance Graph and Local Adaptive Collaborative Representation

Hyperspectral target detection based on transform domain adaptive constrained energy minimization

Hyperspectral Target Detection by Fractional Fourier Transform

Self-supervised learning with deep clustering for target detection in hyperspectral images with insufficient spectral variation prior