Rotating object detection in remote sensing image has made substantial progress. However, the widely used feature extraction and fusion modules cannot accurately represent the spatial location and angle information of remote sensing objects in arbitrary orientation. A detection scheme that accurately expresses spatial angle characteristics is necessary when remote sensing objects are densely distributed. This paper proposes an effective network for rotating object detection in optical remote sensing image (BDR-Net). Firstly, a feature aggregation module (FAM) is designed to refine the feature extraction of rotating objects, which can generate refined feature maps. Secondly, two-dimensional (2-D) Gaussian modeling simulates the rotation angle information of the object. Thirdly, given that the Bhattacharyya distance is mathematically symmetric and rigorous, we design a Bhattacharyya distance based learning module (BDLM). We improve the loss function to more accurately guide the regression of the relative location information of a rotating box. The effectiveness of the proposed method is validated by the results of experiments on three public datasets: DOTA, DIOR, and HRSC2016. The results of our research prove that a Bhattacharyya distance with a Gaussian distribution could be used as an approximate regression loss of the object detection model. This paper offers an original approach to combining object detection with metric learning to apply rotating object detection to instrumentation in a deployable and rapid manner.

Considering the sparsity of hyperspectral images (HSIs), dictionary learning frameworks have been widely used in the field of unsupervised spectral unmixing. However, it is worth mentioning here that existing dictionary learning method-based unmixing methods are found to be short of robustness in noisy contexts. To improve the performance, this study specifically puts forward a new unsupervised spectral unmixing solution. For the reason that the solution only functions in a condition that both endmembers and the abundances meet non-negative constraints, a model is built to solve the unsupervised spectral unmixing problem on the account of the dictionary learning method. To raise the screening accuracy of final members, a new form of the target function is introduced into dictionary learning practice, which is conducive to the growing robustness of noisy HSI statistics. Then, by introducing the total variation (TV) terms into the proposed spectral unmixing based on robust nonnegative dictionary learning (RNDLSU), the context information under HSI space is to be cited as prior knowledge to compute the abundances when performing sparse unmixing operations. According to the final results of the experiment, this method makes favorable performance under varying noise conditions, which is especially true under low signal to noise conditions.

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks.