In recent years, deep convolutional neural networks (DCNNs) have made significant progress in cloud detection tasks, and the detection accuracy has been greatly improved. However, most existing CNN-based models have high computational complexity, which limits their practical application, especially for spaceborne optical remote sensing. In addition, most of the methods cannot make adaptive adjustments based on the structural information of the clouds, and blurred boundaries often occur in the detection results. In order to address these problems, this article proposes a lightweight network (DABNet) to achieve high-accuracy detection of complex clouds, not only a clearer boundary but also lower false-alarm rate. Specifically, a deformable context feature pyramid module is proposed to improve the adaptive modeling capability of multiscale features. Besides, a boundary-weighted loss function is designed to direct the network to focus on cloud boundary information and optimize the relevant detection results. The proposed method has been validated on two data sets: the public GF-1 WFV benchmark and our self-built GF-2 cloud detection data set with higher spatial resolution. The experimental results exhibit that DABNet achieves state-of-the-art performance while only using 4.12M parameters and 8.29G multiadds.

The proposal of Segment Anything Model (SAM) has created a new paradigm for deep learning-based semantic segmentation field, and has shown amazing generalization performance. However, we find it may fail or perform poorly on multimodal remote sensing scenarios, especially the Synthetic Aperture Radar (SAR) images. Besides, SAM does not provide category information of objects. In this paper, we propose a foundation model for multimodal remote sensing image segmentation called RingMo-SAM, which can not only segment anything in optical and SAR remote sensing data, but also identify object categories. First, a large-scale dataset containing millions of segmentation instances is constructed by collecting multiple open-source datasets in this field to train the model. Then, by constructing an instance-type and terrain-type category-decoupling mask decoder, the category-wise segmentation of various objects is achieved. In addition, a prompt encoder embedded with the characteristics of multimodal remote sensing data is designed. It not only supports multi-box prompts to improve the segmentation accuracy of multi-objects in complicated remote sensing scenes, but also supports SAR characteristics prompts to improve the segmentation performance on SAR images. Extensive experimental results on several datasets including iSAID, ISPRS Vaihingen, ISPRS Potsdam, AIR-PolSAR-Seg, etc. have demonstrated the effectiveness of our method.

Recently, satellite video has become an emerging means of earth observation, providing the possibility of tracking moving objects. However, the existing multi-object trackers are commonly designed for natural scenes without considering the characteristics of remotely sensed data. In addition, most trackers are composed of two independent stages of detection and reidentification (ReID), which means that they cannot be mutually promoted. To this end, we propose an end-to-end online framework, which is called TGraM, for multi-object tracking in satellite videos. It models multi-object tracking as a graph information reasoning procedure from the multitask learning perspective. Specifically, a graph-based spatiotemporal reasoning module is presented to mine the potential high-order correlations between video frames. Furthermore, considering the inconsistency of optimization objectives between detection and ReID, a multitask gradient adversarial learning strategy is designed to regularize each task-specific network. In addition, aiming at the data scarcity in this field, a large-scale and high-resolution Jilin-1 satellite video dataset for multi-object tracking (AIR-MOT) is built for the experiments. Compared with state-of-the-art multi-object trackers, TGraM achieves efficient collaborative learning between detection and ReID, improving the tracking accuracy by 1.2 multiple object tracking accuracy. The code and dataset will be available online ( https://github.com/HeQibin/TGraM ).

The detection of garbage dumps is of great significance for environmental protection. Recently, deep learning algorithms have brought impressive improvements for regular object detection. Different from conventional objects, garbage dumps are more inconspicuous and irregular and have the problem of blurred boundaries. To solve these problems, we propose a shape robust anchor-free network (SRAF-Net) that consists of feature extraction, multitask detection, and postprocessing. First, our network leverages the context-based deformable (CBD) module to combine context attention and deformable convolution. The contextual information obtained by context attention enables the network to focus on objects with inconspicuous appearance, while the deformable convolution enhances the feature representation. Then, we propose a multitask detection head to regress irregular garbage dumps in a more accurate and efficient way. The anchor-based methods need to define some anchors with a fixed shape. However, our detection method is anchor-free that learns the shapes of objects from training data. The detection head adaptively generates various shapes of bounding boxes with their classification confidences and localization confidences. Weighted by the localization confidences, we merge bounding boxes during postprocessing, which alleviates the blurred boundaries. In addition, we build a new public data set named garbage dumps data set (GDD) to verify the effectiveness of our method. Extensive experiments on GDD indicate that our method surpasses the existing detection methods in terms of speed and accuracy for the garbage dumps detection task.

Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.

In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge.