Hyperspectral image (HSI) classification is a critical task with numerous applications in the field of remote sensing. Although convolutional neural networks have achieved remarkable success in computer vision, they are still limited in the ability to model long-term dependencies due to small receptive fields. Recently, vision transformers have been used in HSI classification, where multi-head self-attention (MHSA), as the key feature extractor of transformers, learns global dependencies in long-range positions and bands of HSI pixels. Existing vision transformers for classifying HSIs with a large number of bands, however, have some limitations in that features extracted by MHSA may exhibit over-dispersion. In this article, we propose a Group-Aware Hierarchical Transformer (GAHT) for HSI classification, which confines MHSA to the local spatial–spectral context by introducing a new grouped pixel embedding (GPE) module. The GPE emphasizes local relationships within HSI spectral channels, resulting in a global–local fashion from a spatial–spectral context for HSI classification. In addition, we construct our transformer in a hierarchical manner, which can significantly improve classification accuracy with only a few parameters. Extensive experiments on four benchmark HSI datasets demonstrate that the proposed method outperforms state-of-the-art HSI classification algorithms. The source code is available at https://github.com/MeiShaohui/Group-Aware-Hierarchical-Transformer .

Deep neural networks (DNNs) have become essential for aerial detection. However, DNNs are vulnerable to adversarial examples, which pose great security concerns for security-critical systems. Researchers recently devised adversarial patches to evaluate the vulnerability of DNNs-based aerial detection methods physically. Nonetheless, adversarial patches generated by existing algorithms are not strong enough and extremely time-consuming. Moreover, the complicated physical factors are not accommodated well during the optimization process. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed to alleviate the above problems, which achieves state-of-the-art performance in both accuracy and efficiency. Specifically, the AP-PA aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be hidden from being detected. Furthermore, the adversarial patch is also gifted with attack effectiveness against all targets of the same class with a patch outside the target (No need to smear targeted objects) and robust enough in the physical world. In addition, a new loss is devised to consider more available information of detected objects to optimize the adversarial patch, which can significantly improve the patch’s attack efficacy (Average precision drop up to 87.86% and 85.48% in white-box and black-box settings, respectively) and optimizing efficiency. We also establish one of the first comprehensive, coherent, and rigorous benchmarks to evaluate the attack efficacy of adversarial patches on aerial detection tasks. Finally, several proportionally scaled experiments are performed physically to demonstrate that the elaborated adversarial patches can successfully deceive aerial detection algorithms in dynamic physical circumstances. The code is available at https://github.com/JiaweiLian/AP-PA.

Convolutional neural networks (CNNs) have been demonstrated to be a powerful tool to automatically learn effective features from large datasets. Though features learned in CNNs are approximately scale-invariant, translation-invariant, and position-invariant, their capacity in dealing with image rotations remains limited. In this paper, a novel cyclic polar coordinate convolutional layer (CPCCL) is proposed for CNNs to handle the problem of rotation-invariance for feature learning. First, the proposed CPCCL converts rotation variation into translation variation using polar coordinates transformation, which can be easily handled by CNNs. Moreover, cyclic convolution is designed to completely handle the translation variation converted from rotation variation by conducting convolution in a cyclic shift mode. Note that the proposed CPCCL is capable of generalization and can be used as a preprocessing layer for classification CNNs to learn the rotation-invariant feature. Extensive experiments over three benchmark datasets demonstrate that the proposed CPCCL can clearly handle the rotation-sensitive problem in traditional CNNs and outperforms several state-of-the-art rotation-invariant feature learning algorithms.

Learning discriminative features is of crucial for hyperspectral image (HSI) classification. Though metric learning has been applied to learn effective features in HSI classification tasks, existing metric loss functions only consider distance among features of sample pairs but ignore the feature centers and boundaries in the embedding feature space, which limits the discrimination of learned features. In this paper, a novel metric loss function named center-boundary metric loss (CBML) is proposed to learn more discriminative features so as to improve HSI classification performance. Unlike the existing metric loss functions, CBML not only considers the distance between sample pairs to enhance intra-class similarity and inter-class separability but also pays more attention to the feature centers and boundaries in the embedding feature space that could greatly determine and affect the category of features. Specifically, CBML forces the distance of a sample to its corresponding feature center to be explicitly smaller than that to samples from other classes by a predefined threshold. As a result, the boundaries of different classes will separate an actual distance, which improves the discrimination of learned features. Moreover, in order to improve the training efficiency, a cross mini-batch sampling strategy is further proposed to break through the limitation within the mini-batch by using features between several contiguous mini-batches to sample pairs without increasing the size of the mini-batch. Accordingly, the sampling range of sample pairs is greatly expanded, and the training data is more fully exploited. Experimental results over four benchmark datasets with a typical network for HSI classification demonstrate our proposed method outperforms several state-of-the-arts.

Spectral Super-resolution (SR), which reconstructs high spatial-resolution hyperspectral images (HSIs) from RGB inputs, has been demonstrated to be one of the effective computational imaging techniques to acquire HSIs. Though deep neural networks have shown their superiority in such a complex mapping problem, existing networks generally involve a very complex structure with huge amounts of parameters, resulting in giant memory occupation. In this paper, a lightweight Multi-Resolution Feature Fusion Network (MRFN) is proposed, which adopts a multi-resolution feature extraction and fusion framework to fully explore RGB inputs in different scales of resolution. Specifically, a Lightweight Feature Extraction Module (LFEM), which adopts cheap convolution and attention mechanisms, is constructed to explore different scales of features under a lightweight structure. Moreover, a hybrid loss function is proposed by encountering not only pixel-value level reconstruction error, but also spectral continuity and fidelity. Experiments over three benchmark datasets, i.e., CAVE, ICVL, and NTIRE2022 datasets, have demonstrated the proposed MRFN can reconstruct HSIs from RGB inputs in higher quality with fewer parameters and computational FLOPs compared with several state-of-the-art networks.

Patch-based physical attacks have increasingly aroused concerns. However, most existing methods focus on obscuring targets captured on the ground, and some of these methods are simply extended to deceive aerial detectors. They smear the targeted objects in the physical world with the elaborated adversarial patches, which can only slightly sway the aerial detectors’ prediction and with weak attack transferability. To address the above issues, a novel Contextual Background Attack (CBA) framework is proposed to fool aerial detectors in the physical world, which can achieve strong attack efficacy and transferability in real-world scenarios even without smudging the interested objects at all. Specifically, the targets of interest, i.e. the aircraft in aerial images, are adopted to mask adversarial patches. The pixels outside the mask area are optimized to make the generated adversarial patches closely cover the critical contextual background area for detection, which contributes to gifting adversarial patches with more robust and transferable attack potency in the real world. To further strengthen the attack performance, the adversarial patches are forced to be outside targets during training, by which the detected objects of interest, both on and outside patches, benefit the accumulation of attack efficacy. Consequently, the sophisticatedly designed patches are gifted with solid fooling efficacy against objects both on and outside the adversarial patches simultaneously. Extensive proportionally scaled experiments are performed in physical scenarios, demonstrating the superiority and potential of the proposed framework for physical attacks. We expect that the proposed physical attack method will serve as a benchmark for assessing the adversarial robustness of diverse aerial detectors and defense methods. The code has been released at https://github.com/JiaweiLian/CBA.