Leveraging the extensive training data from SA-1B, the segment anything model (SAM) demonstrates remarkable generalization and zero-shot capabilities. However, as a category-agnostic instance segmentation method, SAM heavily relies on prior manual guidance, including points, boxes, and coarse-grained masks. Furthermore, its performance in remote sensing image segmentation tasks remains largely unexplored and unproven. In this article, we aim to develop an automated instance segmentation approach for remote sensing images based on the foundational SAM model and incorporating semantic category information. Drawing inspiration from prompt learning, we propose a method to learn the generation of appropriate prompts for SAM. This enables SAM to produce semantically discernible segmentation results for remote sensing images, a concept that we have termed RSPrompter. We also propose several ongoing derivatives for instance segmentation tasks, drawing on recent advancements within the SAM community, and compare their performance with RSPrompter. Extensive experimental results, derived from the WHU building dataset, the NWPU VHR-10 dataset, and the SAR Ship Detection Dataset (SSDD) dataset, validate the effectiveness of our proposed method. The code for our method is publicly available at https://kychen.me/RSPrompter .

Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly focus on transformation-based global image augmentation and cost-sensitive algorithms. In this article, we propose a novel data-level solution, namely, Instance-level change Augmentation (IAug), to generate bitemporal images that contain changes involving plenty and diverse buildings by leveraging generative adversarial training. The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images. To achieve this, a building generator is employed to produce realistic building images that are consistent with the given layouts. Diverse styles are later transferred onto the generated images. We further propose context-aware blending for a realistic composite of the building and the background. We augment the existing CD data sets and also design a simple yet effective CD model—CD network (CDNet). Our method (CDNet + IAug) has achieved state-of-the-art results in two building CD data sets (LEVIR-CD and WHU-CD). Interestingly, we achieve comparable results with only 20% of the training data as the current state-of-the-art methods using 100% data. Extensive experiments have validated the effectiveness of the proposed IAug. Our augmented data set has a lower risk of class imbalance than the original one. Conventional learning on the synthesized data set outperforms several popular cost-sensitive algorithms on the original data set. Our code and data are available at https://github.com/justchenhao/IAug_CDNet .

The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images. However, due to human and material resource constraints, the vast majority of remote sensing images remain unlabeled. As a result, it cannot be applied to currently available deep learning methods. To fully utilize the remaining unlabeled images, we propose a Geographical Knowledge-driven Representation (GeoKR) learning method for remote sensing images, improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associated with each remote sensing image are regarded as geographical knowledge to provide supervision for representation learning and network pretraining. An efficient pretraining framework is proposed to eliminate the supervision noises caused by imaging times and resolutions difference between remote sensing images and geographical knowledge. A large-scale pretraining dataset Levir-KR is constructed to support network pretraining. It contains 1431950 remote sensing images from Gaofen series satellites with various resolutions. Experimental results demonstrate that our proposed method outperforms ImageNet pretraining and self-supervised representation learning methods and significantly reduces the burden of data annotation on downstream tasks, such as scene classification, semantic segmentation, object detection, and cloud/snow detection. It demonstrates that our proposed method can be used as a novel paradigm for pretraining neural networks. Codes will be available on https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning .

Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly-applicable super-resolution framework called FunSR, which settles different magnifications with a unified model by exploiting context interaction within implicit function space. FunSR composes a functional representor, a functional interactor, and a functional parser. Specifically, the representor transforms the low-resolution image from Euclidean space to multi-scale pixel-wise function maps; the interactor enables pixel-wise function expression with global dependencies; and the parser, which is parameterized by the interactor’s output, converts the discrete coordinates with additional attributes to RGB values. Extensive experimental results demonstrate that FunSR reports state-of-the-art performance on both fixed-magnification and continuous-magnification settings, meanwhile, it provides many friendly applications thanks to its unified nature. Our code is available at https://github.com/KyanChen/FunSR.

Fine-grained image classification can be considered as a discriminative learning process where images of different subclasses are separated from each other while the same subclass images are clustered. Most existing methods perform synchronous discriminative learning in their approaches. Although achieving promising results in fine-grained visual classification (FGVC) in natural images, these methods may fail in fine-grained ship classification (FGSC) problem in remote sensing (RS) images due to the highly “imbalanced fineness” and “imbalanced appearances” of ships among subclasses. To tackle the issue, we propose an asynchronous contrastive learning-based method for effective FGSC. The proposed method, which we refer to as “Push-and-Pull Network (P ² Net)”, includes a “push-out stage” and a “pull-in stage”, where the first stage forces all the instances to be decorrelated and then the second one groups them into each subclass. A dual-branch network is designed to separate/decorrelate the images with each other, while an integration module is designed to aggregate the decorrelated images into their corresponding subclass together with a proxy-based module designed for acceleration. In this way, the correlation between subclasses can be decoupled, which in turn makes the final classification much easier. Our method can be trained end-to-end and requires no additional annotations other than category information. Extensive experiments are conducted on two large-scale FGSC datasets (FGSC-23 and FGSCR-42). Our method outperforms other state-of-the-art approaches. Ablation experiments also suggest the effectiveness of our design. Our code is available at https://github.com/WindVChen/Push-and-Pull-Network .

Existing deep learning-based remote sensing images semantic segmentation methods require large-scale labeled datasets. However, the annotation of segmentation datasets is often too time-consuming and expensive. To ease the burden of data annotation, self-supervised representation learning methods have emerged recently. However, the semantic segmentation methods need to learn both high-level and low-level features, but most of the existing self-supervised representation learning methods usually focus on one level, which affects the performance of semantic segmentation for remote sensing images. In order to solve this problem, we propose a self-supervised multitask representation learning method to capture effective visual representations of remote sensing images. We design three different pretext tasks and a triplet Siamese network to learn the high-level and low-level image features at the same time. The network can be trained without any labeled data, and the trained model can be fine-tuned with the annotated segmentation dataset. We conduct experiments on Potsdam, Vaihingen dataset, and cloud/snow detection dataset Levir_CS to verify the effectiveness of our methods. Experimental results show that our proposed method can effectively reduce the demand of labeled datasets and improve the performance of remote sensing semantic segmentation. Compared with the recent state-of-the-art self-supervised representation learning methods and the mostly used initialization methods (such as random initialization and ImageNet pretraining), our proposed method has achieved the best results in most experiments, especially in the case of few training data. With only 10% to 50% labeled data, our method can achieve the comparable performance compared with random initialization. Codes are available at https://github.com/flyakon/SSLRemoteSensing .