Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.

Although deep learning has received extensive attention and achieved excellent performances in various of scenarios, it suffers from adversarial examples to some extent. Especially, physical attack poses more threats than digital attack. However, existing researches pay less attention to physical attack of object detection in remote sensing images (RSIs). In this work, we systematically analyze the universal adversarial patch attack for multi-scale objects in the remote sensing field. There are two challenges for adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for adversarial patch to show adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide range of height of photography platform causes that the size of objects diverse a lot, which brings challenges for generating universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method on object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Besides, we raise a scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

Active fire detection is essential for early warning of wildfires to help suppress and mitigate damage. This study presents an AF-Net model based on object-contextual representations (OCR) for active fire segmentation from very high-resolution (VHR) unmanned aerial vehicles (UAVs) remote sensing images. To efficiently detect heat anomalies in forests from large UAV scenes, we have to handle the class imbalance between small active fire pixels and large-area complex background information. Class imbalance affects the model optimization and makes the training process stuck at a local minimum. Our work aims to address this issue by improving the object-contextual feature representations associated with fire in three ways. First, we employ a grid-based sampling strategy by constraining sampling ranges and reducing background samples. It improves the proportion of foreground pixels from 5.6% to 7.9% and maintains at least one active fire pixel in each sample. Then, we simplify the OCR module to strengthen small object representations related to the active fire using a self-attention unit. The OCR module receives multi-scale pixel representations as input from the HRNet-W48 backbone. Lastly, the weighted binary cross-entropy loss and the Lovász hinge loss are combined to improve the detection accuracy by optimizing the foreground IoU. We evaluate the performance of the proposed AF-Net on one aerial active fire benchmark (FLAME dataset). The proposed framework improves the mIoU score from 78.17% (baseline UNet) to 91.14%.

Deep Neural Networks (DNNs) have attained remarkable success in aerial detection tasks, yet they remain susceptible to adversarial samples, posing a significant challenge for their practical applications. While numerous transferable attacks have been proposed, they frequently overlook the essential balance between attack effectiveness and the feasibility of physical implementation. In this paper, we concentrate our efforts on adversarial attacks against aerial detection, crafting transferable adversarial patches that can be implemented in the physical world. To this end, we introduce localized pattern corruptions, such as light spots and shadows, around the target during the training phase. These corruptions could pull the image distributions closer to the decision boundaries of the surrogate model, thereby enhancing the transferability of patches. In addition, we avoid directly optimizing adversarial patterns with traditional gradient-based techniques. Instead, we opt to update the weights of a specialized generator, which employs MLPs as its core component for mapping purposes. To verify the effectiveness of our method, we conduct experiments across both digital and physical domains. The results reveal that our approach outperforms state-of-the-art methods in terms of attack performance. Our code is available at https://github.com/zhangyu13a/CPA .

Vision Transformers (ViTs) have achieved great success in UAV target detection tasks. However, little attention has been paid to the adversarial attack against ViT-based detectors, and the generated adversarial examples cannot take physical realizability and attack transferability into account at the same time. To overcome the limitation, we focus on transferable attacks towards ViT-based detectors in optical UAV-based remote sensing images and generating adversarial examples in the physical world. Concretely, we design unique perturbation patches deployed within and beyond the target object rather than requiring the patches to be aligned with image tokens. To narrow the gap between limited digital samples and complex physical scenarios, we conduct data augmentation on training images at global and local levels. In addition, we propose a novel transferable attack method named Jacobian Matrix Regularization (JMR), which consists of Feature Variance Regularization (FVR) and Attention Weight Regularization (AWR). Specifically, FVR calculates feature variances of different channels within specific layers and then sets the features as zeros for channels with top variances. AWR is achieved by masking the largest self-attention weights. We conduct extensive transferable experiments with typical detectors in both digital and physical UAV-based remote sensing scenarios. The results indicate that our method could achieve competitive transferability compared with state-of-the-art methods.