Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

The complexity of backgrounds, the diversity of object scale and orientation, and the defects of convolutional neural network (CNN) have always been the challenges of oriented object detection in remote sensing images (RSIs). This paper designs a hybrid network model to meet these challenges and further improve the effect of oriented object detection. The inductive bias of CNN makes the network translation invariant, but it is difficult to adapt to RSIs with arbitrary object direction. Therefore, this paper designs a hybrid network, TransConvNet, which integrates the advantages of CNN and self-attention-based network, pays more attention to the aggregation of global and local information, makes up for the lack of rotation invariability of CNN with strong contextual attention, and adapts to the arbitrariness of the object direction of RSIs. In addition, to resolve the influence of complex backgrounds and multi-scale, an adaptive feature fusion network (AFFN) is designed to improve the information representation ability of feature maps with different resolutions. Finally, the adaptive weight loss function is used to train the network to further improve the effect of object detection. Extensive experimental results on the DOTA, UCASAOD, and VEDAI data sets demonstrate the effectiveness of the proposed method.

Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.

To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images.

The existence of adversarial examples highlights the vulnerability of deep neural networks, which can change the recognition results by adding well-designed perturbations to the original image. It brings a great challenge to the remote sensing images (RSI) scene classification. RSI scene classification primarily relies on the spatial and texture feature information of images, making attacks in the feature domain more effective. In this study, we introduce the feature approximation (FA) strategy, which generates adversarial examples by approximating clean image features to virtual images that are designed to not belong to any category. Our research aims to attack image classification models that are trained with RSI and discover the common vulnerabilities of these models. Specifically, we benchmark the FA attack using both featureless images and images generated via data augmentation methods. We then extend the FA attack to multimodel FA (MFA), improving the transferability of the attack. Finally, we show that the FA strategy is also effective for targeted attacks by approximating the input clean image features to the target category image features. Extensive experiments on the remote sensing classification datasets UC Merced and AID demonstrate the effectiveness of the methods in this article. The FA attack exhibits remarkable attack performance. Furthermore, the proposed MFA attack outperforms the success rate achieved by existing advanced targetless black-box attacks by an average of over 15%. The FA attack also performs better compared to multiple existing targeted white-box attacks.