Detecting small air target is an important task in civil aviation. However, the weak characteristics of these targets make detection challenging. Hyperspectral image (HSI), provides a new approach for the small air target detection task due to its strong ability of capturing both spatial and spectral information simultaneously. In this article, we propose a spectral-spatial enhanced local contrast strategy for hyperspectral small air target detection. An unsupervised band selection step based on the local contrast strategy has been designed based on local contrast (LC-UBSM) to choose bands with better distinguish ability between the target and background in HSI. Then, we have developed an improved RX detection algorithm with combined spatial and spectral variance (CSSV-RX) to detect the target while suppressing both background and noise. Experimental results on both real GAOFEN-5 dataset and simulated dataset based on EO-1 (Earth Observing-1) satellite have validated the effectiveness and robustness of the proposed method.

Multimodal change detection (MCD) aims to detect changed areas between the bi-temporal multimodal images such as the RGB, panchromatic (PAN), multispectral (MS), and synthetic aperture radar (SAR) images, which has attracted attention in recent years. However, existing deep learning-based methods for MCD tasks still face several heterogeneity factors, the first one is the spatial resolution differences in multimodal data, which leads to the semantic gap between multimodal features. To solve this problem, we propose the heterogeneous collaborative fusion (HCF) module to integrate the multimodal features with spatial gaps. The other one is the consistency and dissimilarity between multimodal data, which lead to unequal detection contributions. To address this dilemma, we propose the heterogeneous adaptive fusion (HAF) module to fuse multimodal decision-making jointly. In this study, we proposed a heterogeneous fusion network for MCD (HF-MCD) with the HCF and the HAF module. We validate the proposed method on four public available MCD datasets. Extensive experimental results have demonstrated the superior performance of HF-MCD over the state-of-the-art methods.

Small vehicles (SV) detection is crucial for urban security and traffic management. However, detecting such targets from a single image presents significant challenges due to the difficulty in discerning their dynamic movements. In this paper, we propose a deep joint image-level and feature-level processing network, IFNet, designed for detecting changes in SV using bi-temporal hyperspectral images. At the image-level, a new Gumbel Softmax trick (GS)-based band selection strategy is introduced to address the problem of inconsistent spectral resolutions of bi-temporal images. At the feature-level, to tackle the challenge of capturing edge and shape details of SV, we propose a feature-based edge enhancement module, it can extract the target edge using high-level difference features, and the refined change map will be generated with the guidance of the edge map. Moreover, current deep learning-based hyperspectral change detection (HCD) methods are limited by HCD datasets. Therefore, we propose a benchmark dataset, the Hyperspectral Vehicle Change Detection (HVCD) dataset, which consists of 201 pairs of aerial hyperspectral images, each with a size of $256\times 256$ , and exhibits inconsistent spectral resolutions across the bi-temporal data. Extensive experiments conducted on the HVCD dataset demonstrate that our IFNet obtains state-of-the-art performance with an acceptable computational cost.

Hyperspectral anomaly detection (HAD) aims to distinguish between the background and anomalies in a scene, which has been widely adopted in various applications. Deep neural network (DNN)-based methods have emerged as the predominant solution, wherein the standard paradigm is to discern the background and anomalies based on the error of self-supervised hyperspectral image (HSI) reconstruction. However, current DNN-based methods cannot guarantee correspondence between the background, anomalies, and reconstruction error, which limits the performance of HAD. In this article, we propose a novel gated transformer network for HAD (GT-HAD). Our key observation is that the spatial-spectral similarity in HSI can effectively distinguish between the background and anomalies, which aligns with the fundamental definition of HAD. Consequently, we develop GT-HAD to exploit the spatial-spectral similarity during HSI reconstruction. GT-HAD consists of two distinct branches that model the features of the background and anomalies, respectively, with content similarity as constraints. Furthermore, we introduce an adaptive gating unit to regulate the activation states of these two branches based on a content-matching method (CMM). Extensive experimental results demonstrate the superior performance of GT-HAD. The original code is publicly available at https://github.com/jeline0110/ GT-HAD, along with a comprehensive benchmark of state-of-the-art HAD methods.

Multimodal images can provide richer information for a wide range of applications. However, the physical heterogeneity resulted by the difference of spatial resolution pose great challenges for multimodal change detection. To this end, we propose a change detection method called primary modality guided deep neural network (PMGN), integrating multi-resolution and multimodal data. First, we propose the principal modality and rely more on its information. Second, PMGN compensates for the limitations of low spatial resolution modalities through the primary modality guided feature exchange module. Finally, the adaptive decision fusion module enables the multimodal decision-level features to fuse efficiently. Experiments demonstrate the effectiveness and advantages of the proposed approach.

Hyperspectral image (HSI) change detection can detect subtle land surface change information, which is of great significance for promoting the sustainable development of human beings. Different from traditional methods, deep learningbased methods can effectively extract more discriminative features, but the problem of mixed pixels is still a challenge due to the low spatial resolution HSI. In this study, an Adaptive Endmembers Learning (AEL)-based deep unmixing network has been proposed for the change detection task, which can perform an unsupervised unmixing through adaptive endmembers learning and then obtain both the binary and multi-class change detection results. Experiments on the China dataset and the USA dataset have shown that AEL performs better than current state-of-the-art methods.

Due to the complementarity of visible and infrared images, it has become more favorable to fuse these two modalities to improve the object detection accuracy in the remote sensing area. However, there are still some problems to be solved. Most of the existing algorithms focus too much on the local information and ignore long-range information when performing feature extraction on different modalities. Besides, coarse weighted fusion strategies do not fully utilize the information from different modalities, and the fusion structure ignores the importance of intermodal information exchange. To tackle these problems, a cross-modal feature fusion and interaction strategy for the convolutional neural network (CNN)-transformer-based object detection in visual and infrared remote sensing imagery is proposed. We adopt a parallel structure to extract the features of different modalities, separately. In visual and infrared modality, the convolutional layers and transformer encoders are cascaded to fully extract both local and long-range information. The cross-modal feature fusion and interaction module (CFFIM) adopts the attention mechanisms to jointly fuse different modal features at the same scale to improve the diversity of fused features, and the feature interaction enables the sharing of visible and infrared information. Experiments on the VEDAI dataset have demonstrated the effectiveness of the proposed scheme compared to other state-of-the-art algorithms.

Hyperspectral unmixing can handle the mixed pixels in hyperspectral images (HSIs). Shadows of objects in observed areas are recorded by sensors, resulting in an HSI contaminated by shadows. Therefore, shadow pollution is a grievous obstacle for unmixing applications. Although shadow pollution occurs frequently in HSIs, previous unmixing studies have never considered the interference caused by shadows. Hence, mitigating shadow interference for unmixing will be significant for further acquiring subpixel information. In this letter, we employ a generative adversarial autoencoder (GAA) to develop a supervised unmixing method that can substantially reduce the impacts of shadow for unmixing. Specifically, we adopt the GAA to establish an anti-shadow unmixing network (GAA-AS), where the encoder block is used to feature reinforcement, and the decoder serves for abundance estimation. Moreover, we adopt a spectral-aware loss (SAL) as the loss function of adversarial training, which makes the discriminator better capture the difference between pixels. Finally, a softmax layer is adopted for the abundance sum-to-one constraint (ASC). Several experiments verify the effectiveness and advantages of our GAA-AS. In the experiment with shadow-polluted data, the proposed GAA-AS improves accuracies by approximately 70% compared to SOTA approaches in the quantitative experiment with synthetic data, and the impacts of shadow pollution are also significantly alleviated in the experiment with real shadow-polluted HSIs. Additionally, note that the proposed GAA-AS is competitive even when no shadow exists in HSIs, verified by the experiment with shadowless data.

Deep learning (DL)-based unsupervised band selection (UBS) methods have received more attention, but the majority of current approaches face challenges associated with striking a balance between computational burden and the UBS performance, and the spatial-spectral information has not been fully investigated. With the aim of addressing these issues, we have proposed a novel method called spatial-spectrum fully-attention network (SSFAN), which includes a spatial-spectral samples generator (SSSG) and a nearest neighbor scoring (NNS) module. Aiming to improve the UBS performance without a huge computational burden, the SSSG can directly generate numerous nonoverlapped samples for the input of DL model, where the global spatial-spectral information is utilized in a more efficient way. For the purpose of further improving the robustness of SSFAN, the NNS can assign different weights to each band by jointly exploiting the prior knowledge in both spatial and spectral domains. Note that the NNS considered the time consumption when investigating the spatial-spectral prior information, so this does not conflict with the problem of UBS balance. We have conducted experiments on three commonly used remote sensing hyperspectral image datasets, where our proposed methods have shown a more effective and robust performance than current state-of-the-art approaches. The source code will be made publicly available at https://github.com/duang33/SSFAN.

Deep-learning (DL)-based methods have been shown to be effective on the hyperspectral image (HSI) anomaly detection task because of their feature extraction ability. However, current DL-based methods lack an effective means of regularizing the background information. In this article, the hyperbolic space-based autoencoder (HSAE) is proposed for the hyperspectral anomaly detection task. We assume that an effective hierarchical structural representation can better model the HSI in the spatial domain, and this enables the background information to be effectively regularized. Motivated by this idea, the HSAE embeds the HSI into hyperbolic space, which is a non-Euclidean geometry with a constant negative curvature and an exponential growth distance between points. Using a wrapped normal prior distribution, the training of the hidden representation is supervised to preserve more hierarchical features. After the training process, a hyperbolic distance-based anomaly detector (HDB) is introduced to discover anomalies in a more robust way. Experimental results on several popular HSI benchmarks fully demonstrate the superiority of our HSAE.

Hyperspectral anomaly detection (HAD) aims to highlight the pixels that are different from the surrounding pixels without any prior information. However, as a hyperspectral image (HSI) tends to possess a huge data volume in the spectral domain, the dimension curse is inevitable in HAD. The unsupervised band selection (UBS) method is an effective tool to avoid the dimensionality curse in the HAD task. To obtain a more robust band subset without the help of any HAD detectors, we propose a spectral–spatial out-of-distribution (OOD)-based UBS method for HAD (HADUBS), which can acquire the optimal band subset in a more straightforward way. Our key observation is that the OOD term of pixels can reveal the differences and similarities of anomaly representation ability of different bands. Hence, we developed an OOD-based feature subspace representation module to obtain latent feature spaces with a better indication of the anomaly detection ability. Moreover, we introduced a UBS strategy called mutual information (MI)-based local outlier factor (MILOF) to significantly improve the discriminative ability of the selected band subset by investigating the locally sparse prior of anomalies. Extensive experimental results on five common HAD datasets demonstrate the superior performance of HADUBS. The source code will be made publicly available athttps://github.com/duang33/HADUBS.

Measuring the purity of the metal powder is essential to maintain the quality of additive manufacturing products. Contamination is a significant concern, leading to cracks and malfunctions in the final products. Conventional assessment methods focus more on physical integrity rather than material composition and can be time-consuming. By capturing spectral data from a wide frequency range along with the spatial information, hyperspectral imaging (HSI) can detect minor differences in terms of temperature, moisture, and chemical composition to tackle this challenge. In this article, we explore the application of HSI in conjunction with machine learning for nondestructive inspection of metal powders. By employing near-infrared and visible HSI cameras, we introduce the utilization of HSI for this purpose. We delve into the technical challenges encountered and present detailed solutions through three case studies, including the establishment of a spectral dictionary, contamination detection, and band selection analysis. Our experimental results demonstrate the immense potential of HSI and its synergy with machine learning for nondestructive testing in powder metallurgy, particularly in meeting the requirements of industrial manufacturing environments.

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still lacking. The lack of proper understanding of the HSI data structure has resulted in the inconsistency in the outcome of UBS. Besides, most of the UBS methods are either relying on complicated measurements or rather noise sensitive, which hinder the efficiency of the determined band subset. In this article, an adaptive distance-based band hierarchy (ADBH) clustering framework is proposed for UBS in HSI, which can help to avoid the noisy bands while reflecting the hierarchical data structure of HSI. With a tree hierarchy-based framework, we can acquire any number of band subset. By introducing a novel adaptive distance into the hierarchy, the similarity between bands and band groups can be computed straightforward while reducing the effect of noisy bands. Experiments on four datasets acquired from two HSI systems have fully validated the superiority of the proposed framework.

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The idea of the CAE-UBS is quite straightforward, which does not rely on any complicated strategies or metrics. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs.