Visible spectrum images capture limited information from just three discrete bands, often resulting in suboptimal performance in underwater depth estimation (UDE) due to significant information loss from water absorption. In contrast, HSIs, which include hundreds of continuous bands, provide abundant spectral information that offers greater resilience against the adverse effects of water absorption. In this paper, we conduct a comprehensive study to investigate how spectral information can enhance remote sensing UDE through two key aspects: the benchmark dataset and the general framework. For the benchmark dataset, we construct a real-world hyperspectral UDE (HUDE) dataset ATR-HUDE, comprising approximately 500 synchronized hyperspectral and LiDAR data pairs collected from diverse coastal scenes and flight altitudes. Regarding the general framework, we integrate recent advances in state space models and physical imaging models to design a novel HUDE framework named HUDEMamba that estimates underwater depth using both model-driven and data-driven approaches. Experimental results on the constructed benchmark dataset validate the potential of HUDE and the effectiveness of HUDEMamba.

Deep learning-based hyperspectral target detection methods commonly face challenges such as insufficient target samples and inadequate use of spatial context. To address these limitations, we propose a novel hyperspectral target detection approach leveraging spatial-spectral graph convolutional networks. First, we introduce an innovative sample augmentation strategy utilizing pre-detection and target implantation, which can simulate different backgrounds around the target and expand the target sample, thereby enhancing the representation ability of the model. Next, a graph-based representation strategy is proposed to integrate spatial and spectral information. Finally, we develop four specialized graph network detectors (GCND, GATD, GCN-GAT1, and GCN-GAT2) with structural optimizations involving multi-scale feature fusion and dynamic neighborhood adjustments. Extensive experiments demonstrate the superior detection performance of our method compared to existing techniques, highlighting its practical significance.

High-resolution (HR) remote sensing image change detection aims to identify surface changes; however, complex scenes and irregular object edges pose significant challenges to achieving accurate results. Existing methods leverage upsampling, downsampling, or dilated convolution to capture multiscale spatial features and fuse fine-scale details into coarse-scale features using concatenation, addition, or skip connections to enhance edge information. However, these direct fusion operations can cause fine edge details to be overshadowed by dominant regional features. To address this, we propose an edge-guided change detection (EGCD) network that improves edge preservation and detection accuracy. In the encoding stage, a region-edge feature extraction module (REM) is introduced to extract regional and edge features in parallel using a two-branch structure for each temporal image. The edge and regional features from the two temporal images are then fused independently via a separation feature fusion (SFF) module, preventing fine edge details from being dominated by regional features. In the decoding stage, a edge enhancement upsampling (EEU) module uses edge features to guide the reconstruction of regional features, ensuring precise boundary delineation. Experiments on public datasets validate the effectiveness and robustness of the proposed network.

Hyperspectral imaging (HSI) provides fine-grained spectral information essential for material identification and target detection, particularly in complex environments such as underwater scenarios. However, hyperspectral underwater target detection (HUTD) remains challenging due to severe spectral distortions and variability introduced by wavelength-dependent absorption and the dynamic nature of aquatic environments. Existing separation-based and characterization-based methods are often constrained by weak signal responses or a heavy reliance on accurate environmental parameter estimation, which is difficult to achieve in practice. To overcome these limitations, we propose PCL-HUTD, a novel physics-informed curriculum learning framework for robust underwater target characterization without requiring explicit environmental modeling. PCL-HUTD integrates a physics-guided target construction module with a hard-sample aware contrastive learning strategy, enhanced by unsupervised clustering and a perturbation-consistency based sample selection mechanism. Furthermore, a closed-loop curriculum learning paradigm is introduced to progressively refine target representations throughout training. Extensive experiments on three real-world HUTD datasets demonstrate that PCL-HUTD achieves state-of-the-art performance in both detection accuracy and robustness, particularly under challenging conditions with strong background interference. These results validate the effectiveness of our parameter-free, physics-informed approach for underwater hyperspectral target detection.

Detection of targets for hyperspectral images (HSIs) persists as a fundamental task in remote sensing image processing. Exploring the discriminative ability of deep Siamese networks to distinguish targets from backgrounds is the mainstream method for target detection in HSIs. Nevertheless, these methods enhance the discriminative ability of networks by learning the separability distance between the target and the overall backgrounds, where the backgrounds are considered as a single category. As a result, they may struggle to effectively suppress backgrounds with solely subtle spectral differences from the target, resulting in a limited separability performance, and the inability to accurately detect the targets. To alleviate this problem, we propose a novel subtle spectral difference discriminative deep metric learning-based target detector for HSIs (denoted as S2D3ML) in this work. The proposed S2D3ML constructs a deep metric learning framework embedded with a discriminative constraint to learn a deep metric feature space for addressing limited separability, in which the subtle feature differences between targets and different ground objects can be distinguished. In addition, we investigate a new multi-block sparse representation score-based strategy to obtain sufficient samples and spectral centers of backgrounds for training the S2D3ML framework. Finally, the detection of targets is executed within the learned metric space. A comprehensive suite of experiments is rigorously conducted on four benchmark datasets, and the results indicate that the S2D3ML achieves superior performance in HSIs target detection.

The combined sparse and collaborative representation-based algorithm is one of the most effective methods among hyperspectral target detection methods based on representation and dictionary learning. It encourages target atoms to compete with each other and background atoms to collaborate in the representation. However, this method suffers from several drawbacks. In sparse representation, an overcomplete dictionary is necessary, whereas, in collaborative representation, non-negative coefficients are required. Besides, the local dual window approach may result in impure background dictionaries obtained from the outer window. To address these issues, we propose a novel approach for hyperspectral target detection, referred to as the global overcomplete dictionary-based sparse and nonnegative collaborative representation (GODSNCR) detector. First, a hierarchical density clustering algorithm is used to complete the dictionary atom extraction to construct a joint overcomplete dictionary to satisfy the dictionary overcompleteness problem required for sparse representation. Second, a nonnegative constraint on the coefficient matrix and a “sum to one” constraint for the joint representation are incorporated to make it more consistent with the physical meaning. Finally, the limitation of the local dual window approach is overcome by substituting the local background dictionary with a global background dictionary. Through the aforementioned strategies, we can use a joint overcomplete dictionary for achieving the sparse representation of targets and utilize a global background dictionary for the collaborative representation of background, the final detection results are obtained by calculating the residuals. The experimental results clearly demonstrate that the proposed algorithm has significant improvement in detection accuracy and strong robustness compared to other typical representation-based hyperspectral target detection methods. Our model will be available at https://github.com/Chenxing-Li/GODSNCR.

Hyperspectral underwater target detection (HUTD) is a promising and challenging task in remote sensing image processing. Existing methods face significant challenges when adapting to nearshore environments, where cluttered backgrounds hinder the extraction of target signatures and exacerbate signal distortion. Hyperspectral unmixing (HU) demonstrates potential effectiveness for nearshore underwater target detection (UTD) by simultaneously extracting water background endmembers and separating target signals. To this end, this article investigates a novel nonlinear unmixing network for hyperspectral UTD, denoted as nonlinear unmixing network for hyperspectral-UTD (NUN-UTD), in which a well-designed autoencoder-based unmixing network is used to obtain the abundance map as the detection result. To address the weak underwater target signals, a target prior spectral preservation scheme is employed to guide the unmixing network in learning the accurate target abundance. Besides, to address the complexity of the nearshore environment, a pseudomixed data classification constraint is incorporated into the objective function to enhance the discriminative capability between the background and the target. Moreover, we adopt an additive postnonlinear model in the decoder to deal with the interactions between underwater spectra to account for the nonlinear effects between spectra of underwater substances. To validate the effectiveness of the proposed method, we constructed a hyperspectral dataset for nearshore UTD. Extensive experiments conducted on three real-world datasets and one simulated dataset demonstrate that our method achieves outstanding performance in HUTD.

Hyperspectral target detection (HTD) is a promising yet challenging endeavor in remote sensing image processing. Representation learning-based detectors have become one of the mainstream methods to address the task. However, these methods often suffer from weak separability between the target and the background, which results in inferior target detection performance. The reason is that their detection model cannot effectively distinguish the subtle differences between the target and the background. To tackle this issue, this article proposes a new weighted discriminative collaborative competitive representation (WDCCR) model for HTD. In WDCCR, the separability between targets and backgrounds is enhanced by integrating discriminative, competitive, and weight constraints. Meanwhile, to obtain pure background pixels for the representation model, we investigate a new category-based pixel selection method with target orthogonal purification (CPSTOP). The proposed WDCCR target detector is evaluated on six hyperspectral datasets. Experimental results demonstrate that WDCCR outperforms other advanced methods, achieving good detection performance in HTD. The code will be available athttps://github.com/liurongwhm.

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection still faces an important bottleneck, that is, subpixel target detection. In this article, we propose a new detector by learning single spectral abundance for hyperspectral subpixel target detection (denoted as LSSA). Different from most existing hyperspectral detectors that are designed based on a match of the spectrum assisted by spatial information or focusing on the background, the proposed LSSA addresses the problem of detecting subpixel targets by learning a spectral abundance of the target of interest directly. In LSSA, the abundance of the prior target spectrum is updated and learned, while the prior target spectrum is fixed in a nonnegative matrix factorization (NMF) model. It turns out that such a way is quite effective to learn the abundance of subpixel targets and contributes to detecting subpixel targets in hyperspectral imagery (HSI). Numerous experiments are conducted on one simulated dataset and five real datasets, and the results indicate that the LSSA yields superior performance in hyperspectral subpixel target detection and outperforms its counterparts.

In hyperspectral target detection, the conventional metric learning-based algorithms provide unique advantages in detecting targets as they do not require specific assumptions and adapt to the condition of limited training samples. Nevertheless, they usually learn a linear transformation for metric space, which is unable to capture nonlinear mapping where the hyperspectral imageries possess, especially occurs in the spectra variability and nonlinear mixing problems. To alleviate this limitation, this study investigates a new spatial-spectral adaptive sample generation and deep metric learning-based method for hyperspectral target detection (denoted as DMLTD). The proposed DMLTD employs a spatial-spectral adaptive sample generation strategy and subpixel synthetic method for background sample generation and target sample augmentation, respectively. With sufficient samples, the proposed DMLTD trains a deep discriminative metric learning network to learn hierarchical nonlinear mappings, so that to address the spectra variability and nonlinear mixing problems, thus exploiting discriminative information between targets and backgrounds for detection. Experiments and analyses conducted on three real-world hyperspectral datasets indicate that our DMLTD yields competitive performance in hyperspectral image target detection.

Hyperspectral image (HSI) anomaly detection is an important task in remote sensing domain. In recent years, many scholars have been addicted to constructing deep network-based methods for hyperspectral anomaly detection and have developed numerous related methods. Many of them are designed based on autoencoder, which aims to reconstruct a stable background to identify anomalies. However, these autoencoder-based methods suffer from some problems, such as ignoring the inter-band correlation in HSI. That is, the hyperspectral image presents spectral similarity as well as redundancy between the contiguous bands, which would affect the reconstruction of the HSI. Moreover, the current anomaly detectors lack the use of spatial contextual information that exists in the pixel neighbor region when constructing the detector. To tackle these problems, this study presents a spatial-spectral joint reconstruction with the inter-band correlation based anomaly detector (denoted as SSRICAD) for hyperspectral images. We first divide the original HSI into several sub-HSIs by a band cross-grouping strategy to reduce the redundancy and impose the inter-band correlation constraint into the reconstruction process. Then, an outlier removal constraint is added to alleviate anomaly contamination, which could help rebuild a more stable and pure background component. Finally, spatial information is extracted from the pixel neighbor region to contribute to the spatial-spectral joint reconstruction and further enhance detection performance. Extensive experiments on three benchmark hyperspectral datasets indicate that the proposed SSRICAD can achieve superior performance in anomaly detection.

The limited training sample has become a great challenge for hyperspectral target detection with deep learning-based methods. In this paper, a long short-term memory based hyperspectral target detector is proposed. To handle the insufficient background training samples, an endmember extraction based pixel selection strategy is proposed to select background pixels from the entire image. For the target training samples, we utilize a synthesis method to generate sufficient target samples using the given target spectrum and the extracted background samples. Then the obtained target and background samples are fed into the well-designed long short-term memory network to learn the discriminative ability. Finally, the detected pixels are classified by the well-trained LSTM network and the detection results are achieved. The experiments on the Muufl and Nuance data sets demonstrate the superiority of the proposed LSTMTD in target detection.

In this paper, an encoder-decoder long short-term memory network-based anomaly detector (denoted as EDLAD) is proposed for hyperspectral images. The proposed EDLAD aims to simultaneously alleviate anomaly contamination and build a stable background component for anomaly detection. To reduce anomaly contamination, the EDLAD first utilizes a well-designed encoder-decoder LSTM to reconstruct the hyperspectral image. Based on the concept that the anomaly pixels occupy an extremely small fraction of the image, the well-designed encoder-decoder LSTM network tends to maintain the background and alleviate anomaly during the reconstruction process since the whole image is employed for training the network. Then the dimension reduction is used to further alleviate the anomaly contamination and build a stable background component. Finally, the EDLAD applies the Mahalanobis distance differences to detect the probable anomalies. The experiments on two benchmark hyperspectral images demonstrate the superiority of the EDLAD in anomaly detection.

In this article, a single-spectrum-driven binary-class sparse representation target detector (SSBSTD) via target and background dictionary construction (BDC) is proposed. The SSBSTD leans upon the binary-class sparse representation (BSR) model. Due to the fact that a background spectrum usually consists in background samples composed low-dimensional subspace and a target spectrum also consists in target samples composed low-dimensional subspace, only background samples should be used for sparsely representing the test pixel under the target absent hypothesis and the samples from target-only dictionary for target present hypothesis. To alleviate the problem that there are insufficient available target samples in the sparse representation model, this article proposed a predetection method to construct the target dictionary utilizing the given target spectrum. With regard to the BDC, we proposed an approach based on the classification to generate a global over-complete background dictionary. The detection output is composed of the residual difference between the BSR. Extensive experiments were made on four benchmark hyperspectral images and the experimental results indicate that our SSBSTD algorithm demonstrates superior detection performances.

In this article, a two-stream convolutional network-based target detector (denoted as TSCNTD) for hyperspectral images is proposed. The TSCNTD utilizes the two-stream convolutional networks to extract abundant spectral information in hyperspectral images. For the background samples, the TSCNTD finds enough typical background pixels via a hybrid sparse representation and classification-based pixel selection strategy in the entire image. To tackle the problem under limited target samples, a novel synthesis method is proposed to generate sufficient target samples with a target priori and some typical background pixels. Once the target and background samples are obtained, then the designed two-stream convolutional networks were trained with a target priori, target samples, and background samples. During training, a target priori and a target sample, which construct a positive training sample, are considered as two inputs of the two-stream convolutional networks, while a target priori and a background sample construct a negative training sample. During testing, the test samples, which are constructed by a target priori and the detected pixels, are classified by the well-trained network. The outputs of the network constitute the final detection result of the TSCNTD. Extensive experiments were made on four benchmark hyperspectral images. The experimental results indicate that the TSCNTD can achieve superior performances in target detection.

Hyperspectral target detection refers to an approach that tries to locate targets in a hyperspectral image (HSI) on the condition of given targets spectrum, which plays an important role in hyperspectral remote sensing image processing. In this letter, we propose a binary-class collaborative representation-based detector. The proposed algorithm uses the concept that each background pixel can be approximately represented by its adjacent pixels within a sliding dual-window, and each target pixel can also be approximately represented by some pixels of the image; we use the given target pixels to represent it. Before estimating each background pixel, a background dictionary purification process is proposed to further improve the detector performance. The proposed algorithm was tested on three benchmark HSI data sets, and the experimental results show that the proposed algorithm demonstrates outstanding detection performances when compared with other state-of-the-art detectors.