Deep learning methods have made significant progress in ship detection in synthetic aperture radar (SAR) images. The pretraining technique is usually adopted to support deep neural networks-based SAR ship detectors due to the scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hard to obtain a good ship detector because of different imaging perspectives and geometry. In this article, to resolve the problem of inconsistent imaging perspectives between ImageNet and earth observations, we propose an optical ship detector (OSD) pretraining technique to transfer the characteristics of ships in earth observations to SAR images from a large-scale aerial image dataset. On the other hand, to handle the problem of different imaging geometry between optical and SAR images, we propose an optical-SAR matching (OSM) pretraining technique, which transfers plentiful texture features from optical images to SAR images by common representation learning on the OSM task. Finally, observing that the OSD pretraining-based SSD has a better recall on sea area while the OSM pretraining-based SSD can reduce false alarms on land area, we combine the predictions of the two detectors through weighted boxes fusion to further improve detection results. Extensive experiments on four SAR ship detection datasets and three representative convolutional neural network-based detection benchmarks are conducted to show the effectiveness and complementarity of the two proposed detectors, and the state-of-the-art performance of the combination of the two detectors. The proposed method won the sixth place of ship detection in SAR images in the 2020 Gaofen challenge.

Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, most existing HAD methods require training a separate model for each HSI, resulting in poor generalization in practical applications. This paper starts the first attempt to study a new and generalizable background learning problem without labeled samples. We present a novel solution BSDM (background suppression diffusion model) for HAD, which can simultaneously learn latent background distributions and generalize to different datasets for suppressing complex background. It is featured in three aspects: (1) For the complex background of HSIs, we design pseudo-background noise and learn the potential background distribution in it with a diffusion model (DM). (2) For the generalizability problem, we apply a statistical offset module so that the BSDM adapts to datasets of different domains without labeling samples. (3) For achieving background suppression, we innovatively improve the inference process of DM by feeding the original HSIs into the denoising network, which removes the background as noise. Our work paves a new background suppression way for HAD that can improve HAD performance without the prerequisite of manually labeled data. Assessments and generalization experiments of four HAD methods on several real HSI datasets demonstrate the above three unique properties of the proposed method. Our project is available at https://github.com/majitao-xd/BSDM-HAD.

Non-line-of-sight (NLOS) imaging is attractive for its potential applications in autonomous vehicles, robotic vision, and biomedical imaging. NLOS imaging can be realized through reconstruction or recognition. Recognition is preferred in some practical scenarios because it can classify hidden objects directly and quickly. Current NLOS recognition is mostly realized by exploiting active laser illumination. However, passive NLOS recognition, which is essential for its simplified hardware system and good stealthiness, has not been explored. Here, we use a passive imaging setting that consists of a standard digital camera and an occluder to achieve a NLOS recognition system by deep learning. The proposed passive NLOS recognition system demonstrates high accuracy with the datasets of handwritten digits, hand gestures, human postures, and fashion products (81.58 % to 98.26%) using less than 1 second per image in a dark room. Beyond, good performance can be maintained under more complex lighting conditions and practical tests. Moreover, we conversely conduct white-box attacks on the NLOS recognition algorithm to study its security. An attack success rate of approximately 36% is achieved at a relatively low cost, which demonstrates that the existing passive NLOS recognition remains somewhat vulnerable to small perturbations.

Airplane detection in synthetic aperture radar (SAR) images has drawn much attention owing to the success of deep learning methods. However, the development of fine-grained airplane detection in SAR images is still in a dilemma due to the small interclass variance and the large intraclass variance in complex scenes with strong interference from the background. In addition, the class imbalance problem in multiclass fine-grained airplane recognition also significantly limits the direct application of general deep-learning-based airplane detectors. This article proposes two effective methods to tackle the above two problems, respectively. First, we propose a sparse attention-guided fine-grained pyramid module to simultaneously sample discriminative local features scattered in multiscale layers and adaptively aggregate them with fine-grained attention to better classify subordinate-level airplanes with multiple scales. Second, a simple class-balanced copy-paste data augmentation strategy, which randomly copies an airplane of one category and pastes it onto an image according to the classwise probability, is proposed for class balance. Finally, extensive experiments on one public dataset and three representative deep-learning-based detection benchmarks are conducted to show the effectiveness and generalization of the two proposed methods. The combination of these two methods based on the cascade R-CNN benchmark also won the fifth place in fine-grained airplane detection in SAR images in the 2021 GaoFen Challenge.