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Oriented object detection (OOD) in remote sensing images (RSIs) remains a challenging work due to an arbitrary orientation of instances. Learning rotation-invariant features is critical in modeling a fixed descriptor for instances with its rotated variants. However, most existing methods construct the descriptor from the perspectives of data or feature augmentation, but ignore the exploration of potentially useful supervision information inside the detection algorithm. In this article, we propose an object-wise rotation-invariant semantic representation (ORSR) framework, which synergizes the exploration of latent supervision, rotation-invariant learning, and guided attention mechanism into a unified network to boost the performance of OOD in RSIs. First, supervised by our constructed pseudo-ground truth of segmentation masks, a semantic segmentation branch is built along with the detection algorithm to refine the representation of backbone features. Moreover, a consistency loss function is proposed to encourage the segmentation branch to make fixed predictions for backbone features with its rotated variants. Considering that segmentation predictions remain the same affine transformations before and after rotating, we further construct a Kullback-Leibler (KL) divergence-based similarity loss function to encourage the network to model the rotation-invariant features. Finally, we separate the “object” descriptor from the segmentation predictions to extend the implicit constraint in our proposed semantic segmentation branch. The separated “object” descriptor not only involves the spatial regularizer to emphasize the high-responsive regions in the image but also can be guided by the constructed consistency loss function. We evaluate our proposed ORSR on the challenging DOTA, DIOR-R, and HRSC2016 datasets. Extensive experiments demonstrate that the proposed ORSR achieves competitive performance compared to other single-scale and multiscale (MS) detection methods.

In hyperspectral cross-scene classification tasks, it is often challenging to obtain target domain samples during the training phase. Therefore, models need to be trained on one or multiple source domains and achieve good generalization performance on unknown target domains, known as domain generalization. The presence of domain shift limits the model’s generalization across different domains, while the unknown target domain makes it difficult to accurately characterize the distribution differences between domains. To address this issue, we propose a generalization network based on nonlinear sample generation. The network divides the sample features into invariant features and variant features and generates samples by applying nonlinear transformations to the variant features. To ensure the quality of the generated samples, we introduce contrastive learning into the model. It ensures consistency in similarity between the generated samples and the source samples while maintaining a certain degree of dissimilarity. Experiments conducted on four cross-domain adaptive scenarios demonstrate the superior performance of our proposed method.