Aircraft classification in synthetic aperture radar (SAR) images plays a considerable role in global region management and surveillance. Recently, deep learning has been applied to solve the classification problem and made significant progress. Due to the imaging variability at different angles and component scattering discreteness in SAR images, previous works have had difficulty in achieving desirable classification results. To address these issues, we study the positional and semantic relationship between the scattering points and propose an innovative scattering topology network (ST-Net) in this article. First, considering the diversity of imaging results caused by different target attitude angles, we extract and transform the scattering cluster centers to update the information of various categories. It can guide the model to strengthen the discriminative features and mitigate the impact of imaging variability on classification performance. Second, a novel scattering topology module (STM) is introduced to model the spatial relationships and semantic information interaction of discrete scattering points. In this process, the topology relations and scattering characteristics are enhanced for further accurate classification. Third, context attention excitation (CAE) is designed to capture significant global and semantic information, which is conducive to suppressing background interference and reducing category confusion. In conclusion, the ST-Net is presented with the SAR imaging mechanism and the topology geometric representation of aircraft. We construct the SAR aircraft category dataset (SAR-ACD) and conduct extensive experiments on it to show the effectiveness of ST-Net, which illustrates that our method achieves superior classification performance.

Deep learning approaches have contributed to the rapid development of remote sensing (RS) image interpretation. The most widely used training paradigm is to use ImageNet pretrained models to process RS data for specified tasks. However, there are issues such as domain gap between natural and RS scenes and the poor generalization capacity of RS models. It makes sense to develop a foundation model with general RS feature representation. Since a large amount of unlabeled data is available, the self-supervised method has more development significance than the fully supervised method in RS. However, most of the current self-supervised methods use contrastive learning, whose performance is sensitive to data augmentation, additional information, and selection of positive and negative pairs. In this article, we leverage the benefits of generative self-supervised learning (SSL) for RS images and propose an RS foundationmodel framework called RingMo, which consists of two parts. First, a large-scale dataset is constructed by collecting two million RS images from satellite and aerial platforms, covering multiple scenes and objects around the world. Second, we propose an RS foundation model training method designed for dense and small objects in complicated RS scenes. We show that the foundation model trained on our dataset with RingMo method achieves state-of-the-art (SOTA) on eight datasets across four downstream tasks, demonstrating the effectiveness of the proposed framework. Through in-depth exploration, we believe it is time for RS researchers to embrace generative SSL and leverage its general representation capabilities to speed up the development of RS applications.

Remarkable progress based on deep neural networks has been achieved on the semantic segmentation in remote sensing images. However, pixel-level labeling is expensive for remote sensing images. Semi-supervised semantic segmentation becomes an alternative approach to reduce the cost of annotation, and it is crucial to utilize efficiently a large number of unlabeled data. Nevertheless inevitably, there is the unbalanced class distribution between labeled and unlabeled data of remote sensing scene. Existing semi-supervised methods train unlabeled images in isolation from labeled images and only learn reliable pixel pseudo-labels, leading to underutilization of unlabeled images. This article proposes a novel semi-supervised semantic segmentation approach based on label propagation and contrastive regularization for remote sensing images. Specifically, the unlabeled images are augmented by randomly copy-pasting the class regions from labeled images. A prototype feature constraint module is used to enforce the constraint on the pixel features of unlabeled images relying on the prototype features from labeled images, achieving feature alignment on the entire dataset. Furthermore, we present the region contrastive learning module that guides the model to learn feature consistency under different perturbations and compact feature representations over class regions on unlabeled images. Extensive experimental results on multiple remote sensing datasets demonstrate that our proposed approach achieves superior performance compared with state-of-the-art semi-supervised semantic segmentation methods.

Segmentation incremental learning has received a lot of attention in recent years due to the ability to overcome the problem of catastrophic forgetting. Our study found that differences in label number distribution affect the performance of segmentation incremental learning. Because the labels for pixels of the old category are marked as background when the model is trained on the new tasks, the label number distribution is inconsistent with static learning that is considered to be the upper bound on incremental learning, which hinders the mitigation of the catastrophic forgetting problem. In response to the above problems, we propose an incremental learning method named SIL-LAND, which improves the accuracy by making the label number distribution of our method close to that of static learning. From the perspective of high-level semantic labels, we propose the prototype update mechanism for the problem that non-adaptive representative prototypes ignore the sample diversity of semantic categories in remote sensing images. By compensating for the difference in label number distribution at the feature level, the distance between the prototype and the actual class center is reduced; Aiming at the lack of semantic consistency between feature vectors and prototypes, we propose a similarity measure module to increase the intra-class similarity between the prototype and corresponding feature vectors. From the perspective of one-hot labels, we propose label reconstruction, including foreground screening and background padding to make the number distribution of one-hot labels as close as possible to that of static learning. A series of experimental results demonstrate the effectiveness of our method.

In recent years, more concerns are shed on the lightweight detection model in remote sensing (RS), but it is difficult to reach a competitive performance relative to the deep model. Knowledge distillation has been verified as a promising method, which can promote the performance of the lightweight model without extra parameters. While there are two key issues of detection distillation, one is the sample selection, the other is the knowledge selection. Since the varying object size and complex features in RS, the existing methods based on the fixed threshold are incapable of selecting the optimal distillation samples and they also ignore the potential multivariate knowledge among RS samples simultaneously. In this paper, we propose a statistical sample selection and multivariate knowledge mining framework. The statistical sample selection module formulates the task as the modeling and splitting the probability distribution of sample selection cost, which is more suitable for dynamically choosing multiscale samples in RS and eliminates the distortion of previous static distillation selection. Furthermore, to mine the complex feature knowledge of samples in RS, we design a multivariate knowledge mining module, in which knowledge includes explicit and implicit knowledge. The proposed module validly deliver the core knowledge from the teacher model to the lightweight model. Massive experiments on three challenging RS datasets (DOTA, NWPU VHR-10, DIOR) prove that our method achieves state-of-the-art performance.

Cloud detection relying on deep convolutional neural networks obtains remarkable accuracy gains at the expense of high computation and storage costs, which are difficult to deploy to resource-constrained devices, such as intelligent satellites. Recently, knowledge distillation (KD) has been a promising solution for compact model. However, most existing KD methods only transfer the feature relationship of pairwise pixel which fails to cope with thin clouds and cloud-like objects in complex scenes. Furthermore, those KD methods directly imitate the output of complicated model regardless of the correctness. In this article, we propose a novel Category Correlation and Adaptive Knowledge Distillation (CAKD) framework for the lightweight cloud detection network. We design a category relational context (CRC) module to refine the structured pixel-category correlation from the teacher and student network. Then, we perform the category correlation distillation (CCD) to make the student model better address the intra-class consistency and the inter-class difference, thus reducing the category confusion. Besides, a pixel-adaptive distillation (PAD) module is utilized to adaptively transfer the soft-output knowledge of teacher model by extracting the teacher’s pixel prediction probability. Extensive experiments on Landsat 8, Landsat 7, Gaofen-2, Gaofen-1 and Google Earth dataset report the effectiveness and universality of our distillation method. The CAKD allows MobileNetV2 with 2.31M parameters and 4.63G FLOPs to outperform advanced cloud detection methods without the added inference overhead.