Deep learning approaches have contributed to the rapid development of Remote Sensing (RS) image interpretation. The most widely used training paradigm is to utilize ImageNet pre-trained 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 remote sensing. 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 paper, we leverage the benefits of generative self-supervised learning for RS images, and propose a Remote sensing foundation Model 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 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 self-supervised learning and leverage its general representation capabilities to speed up the development of RS applications.

With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.

At present, many infrared target detection approaches focus on designing modules that address the two key characteristics of targets: their weak signals and small size. However, these approaches often fail to fully leverage guided learning for weak and small target content, resulting in sub-optimal detection performance, particularly in terms of shape preservation and target positioning. To tackle this challenge, this paper proposes a multi-branch mutual-guiding learning network (MMLNet) that enhances the accuracy of infrared target detection, even in the absence of clear morphological and textural features in images. The method consists of three branches: edge, positioning, and detection, each of which is designed with a specialized module from a unique perspective. In the detection branch, we introduce a multi-dimensional lossless encoder optimized through a downsampling strategy and multi-level feature fusion to mitigate feature loss in small targets. In the positioning branch, a target positioning strategy is proposed to explicitly identify candidate targets from the image by means of a learnable multi-kernel pattern. In the edge branch, a simple architecture is adopted to enhance the ability of the model to preserve the target shape. To effectively utilize the knowledge of different branches, a mutual-guiding fusion module is developed to adjust information within and between branches. The manner adaptively utilizes the specific knowledge from each input branch. Experiment results demonstrate that the proposed method achieves comparable performance, and the visualization results show the advantages of our method in shape preservation and positioning of the targets. Our code is publicly available at https://github.com/qianngli/MMLNet.

The rapid development of Remote Sensing (RS) technology has produced massive images, which makes it difficult to obtain interpretation results by manual screening. Therefore, researchers began to develop automatic retrieval method of RS images. In recent years, cross-modal RS image retrieval based on query text has attracted many researchers because of its flexible and has become a new research trend. However, the primary problem faced is that the information of query text and RS image is not aligned. For example, RS images often have the attributes of multi-scale and multi-objective, and the amount of information is rich, while the query text contains only a few words, and the information is scarce. Recently, Graph Neural Network (GNN) has shown its potential in many tasks with its powerful feature representation ability. Therefore, based on GNN, this paper proposes a new cross-modal RS feature matching network, which can avoid the degradation of retrieval performance caused by information misalignment by learning the feature interaction in query text and RS image respectively, and modeling the feature association between the two modes. Specifically, to fuse the within-modal features, the text and RS image graph modules are designed based on GNN. In addition, in order to effectively match the query text and RS image, combined with the multi-head attention mechanism, an image-text association module is constructed to focus on the parts related to RS image in the text. The experiments on two public standard datasets verify the competitive performance of the proposed model.

Remote sensing spatiotemporal prediction aims to infer future trends from historical spatiotemporal data, e.g., videos and time series images, has a broad application prospect in many fields. The foundation model is a promising research direction for spatiotemporal information mining because of its robust feature extraction capability, and has made rapid progress in natural scenes. Nevertheless, due to the spatially multi-scale and temporally multi-scale properties in remote sensing data, these methods still encounter bottlenecks when applied to remote sensing. Therefore, we propose a foundation model for remote sensing spatiotemporal prediction via spatiotemporal evolution decoupling, abbreviated as RingMo-Sense. Considering spatial affinity, temporal continuity, and spatiotemporal interaction, we construct spatial, temporal, and spatiotemporal triple-branch prediction networks. Specifically, we use parameter-sharing and progressive joint training strategies to achieve stable long-range prediction and parameter reduction simultaneously. In addition, we build a remote sensing spatiotemporal dataset by collecting various remote sensing videos and time series images. The experimental results on six downstream spatiotemporal tasks demonstrate that the proposed model yields competitive performance.

Few-shot remote sensing image classification entails identifying images using a limited set of labeled data within remote sensing scenes, holding significant theoretical and practical implications. However, owing to the intricacy and variety of remote sensing images, traditional classification methods usually struggle to extract effective features and learn robust classifiers. To address this issue, an end-to-end metric learning framework named attention-based contrastive learning network (ACL-Net) is introduced in this article. Specifically, the attention-based feature optimization (ABFO) module is employed to align and enhance target image features, highlighting the target region and strengthening the network’s feature extraction capability. In addition, the dictionary-based contrastive loss (DBCL) module is assigned to optimize image feature vectors, improving category distinguishability and consequently enhancing classification accuracy. The experimental results on five publicly available few-shot remote sensing classification datasets demonstrate the high competitiveness of our proposed method. Furthermore, it illustrates superior classification accuracy compared to other pertinent few-shot learning algorithms in the five-way one-shot scenario.