Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose a practical framework for automatically generating content-rich synthetic images with ground-truth annotations. By rendering 3-D CAD models, we generate two synthetic aircraft image data sets with wide distribution (Syn N and Syn U). For improving the quality of synthetic images, we propose a multiscale attention module which enhances the Cycle-Consistent Adversarial Network (CycleGAN) in spatial and channel dimensions. Then, we compare the synthetic images before and after translation qualitatively and quantitatively. Experiments on Northwestern Polytechnical University (NWPU) very high resolution (VHR)-10, University of Chinese Academy of Sciences, orientation robust object detection in aerial images (UCAS-AOD), and benchmark for object DetectIon in Optical Remote sensing images (DIOR) data sets demonstrate that synthetic data augmentation can improve the performance of aircraft detection in remote sensing images, especially when real data are insufficient. Synthetic data are available at: https://weix-liu.github.io/ .

Vehicle detection methods based on deep learning have achieved remarkable results on remote sensing images. However, the performance of the detector degrades when the test images are distinct from the training images. Domain adaptive vehicle detection is a promising approach to bridging the domain gap. Existing methods usually adopt fully shared networks, but ignore the problem that features from different domains may be incompatible within a single model. In this letter, we present a novel domain adaptive vehicle detection method based on patch-wise domain-specific channel recalibration (PDSCR). The PDSCR module routes the feature to the corresponding network branch and extracts the channel dependence using separate parameters. In this way, our method can explicitly capture domain-specific information for each domain. Furthermore, we propose a dynamic weighted prototype alignment (DWPA) to avoid the negative effects of false pseudo-labels, especially in the early stage of training. Experimental results of adaptation from our synthetic dataset to three real vehicle detection datasets demonstrate the effectiveness of our method. Code and our synthetic data will be available at https://weix-liu.github.io/.

There are large nonlinear radiance differences between optical and synthetic aperture radar (SAR) from different imaging sensors. When combined with rotational and scale variations, achieving equivariant matching becomes highly challenging. The existing single network models face two primary limitations, insufficient learning of modal invariant features and insufficient characterization of rotation and scale. After task decomposition, we propose a novel rotation and scale equivariant network (RSENet) for optical-SAR image matching, comprising two modules, SAR-to-optical image translation module and equivariant descriptors (ED). First, we propose an image translation network to obtain more beneficial pseudo-optical images for matching. Second, we achieve high-precision matching via equivariant descriptors designed through scale angle estimation (SAE) and descriptor colearning. The network initially corrects image blocks explicitly via scale and angle loss functions that are based on periodic encoding (PE) learning. Subsequently, it employs an attention module based on cross-scale perception and feature channel excitation to extract hierarchical information from the corrected image blocks and to enhance the local feature representation of the descriptors. We validate the effectiveness of RSENet by conducting numerous experiments under combined transformations of various angles and scales. Our proposed method achieves an approximately 28% improvement in the matching success rate compared with existing methods.

Existing cloud detection methods often rely on deep neural networks, leading to excessive computational overhead. To address this, we propose a shallow convolutional neural network (CNN)-Transformer hybrid architecture that limits the maximum downsampling rate to 8×. This design preserves local details while effectively capturing global context through a lightweight Transformer branch. To enhance adaptability across diverse cloud scenes, we introduce two novel statistics-driven modules: statistics-adaptive convolution (SAC) and statistical mixing augmentation (SMA). SAC dynamically generates convolutional kernels based on input feature statistics, enabling adaptive feature extraction for varying cloud patterns. SMA improves model generalization by interpolating channel-wise statistics across training samples, increasing feature diversity. Experiments on four datasets show that the proposed method achieves state-of-the-art performance with 732K parameters and 1G multiply-accumulate operations. Code will be available at https://weix-liu.github.io/ for further research.