Deep learning methods have recently demonstrated their significant capability for synthetic aperture radar (SAR) image change detection. However, with the increase of network depth, convolutional neural networks often encounter some negative effects, such as overfitting and exploding gradients. In addition, the existing deep networks employed in SAR change detection tend to produce a lot of redundant features that affect the performance of the network. To solve the aforementioned problems, this article proposed a deep cascade network (DCNet) for SAR image change detection. On the one hand, a very DCNet is established to exploit discriminative features, and residual learning is introduced to solve the exploding gradients problem. In addition, a fusion mechanism is employed to combine the outputs of different hierarchical layers to further alleviate the exploding gradient problem. Moreover, a simple yet effective channel weighting-based module is designed for SAR change detection. Average pooling and max pooling are used to aggregate channel-wise information. Meaningful channel-wise features are emphasized and unnecessary ones are suppressed. Therefore, the similarity in feature maps can be reduced, and then, the classification performance of the DCNet is improved. Experimental results on four real SAR datasets demonstrated that the proposed DCNet can obtain better change detection performance than several competitive methods. Our codes are available at https://github.com/summitgao/SAR_CD_DCNet.

Recently, the research on drone-view object detection (DOD) has predominantly centered on efficiently identifying objects through cropping high-resolution images. However, it has overlooked the distinctive challenges posed by scale imbalance and a higher prevalence of small objects in drone images. In this paper, to address the challenges associated with the detection of drones (DODs), we introduce a specialized detector called DroneNet. Firstly, we propose a feature information enhancement module (FIEM) that effectively preserves object information and can be seamlessly integrated as a plug-and-play module into the backbone network. Then, we propose a split-concat feature pyramid network (SCFPN) that not only fuses feature information from different scales but also enables more comprehensive exploration of feature layers with many small objects. Finally, we develop a coarse to refine label assign (CRLA) strategy for small objects, which assigns labels from coarse- to fine-grained levels and ensures adequate training of small objects during the training process. In addition, to further promote the development of DOD, we introduce a new dataset named OUC-UAV-DET. Extensive experiments on VisDrone2021, UAVDT, and OUC-UAV-DET demonstrate that our proposed detector, DroneNet, exhibits significant improvements in handling challenging targets, outperforming state-of-the-art detectors.