Cross-domain few-shot scene classification (CDF-SSC) aims to establish cross-domain representation between source and target domains, endowing the model few-shot classification ability on the target domain. Recent studies have improved cross-domain representation learning by incorporating unlabeled target data into semi-supervised training with labeled source data. However, these methods struggle to effectively bridge the domain gap between source and target domains, and lack the discriminative feature description ability for unlabeled target domain data, leading to inferior cross-domain representation learning and affecting the few-shot performance. In this article, a dual-domain representation modeling with prototype contrastive learning (DMPC) structure is proposed to improve the robustness of cross-domain representation learning. In DMPC, first, a dual-domain Gaussian representation modeling is designed to model the feature statistics of both source and target data as multivariate Gaussian distributions rather than fixed values and enrich domain representation by random sampling new feature statistics. It helps bridge the domain gap at the feature level, and improves the model’s robustness and generalization to better address unpredictable variations in the target domain. Second, a pseudo-prototype contrastive learning branch is proposed to improve the discriminability of representation for limited unlabeled target data. By leveraging pseudo-prototypes derived from the classifier’s weights as dynamic anchors, it refines feature representation by clustering features of the same pseudo-class and separating those of different pseudo-classes, strengthening the model’s ability to capture distinct and consistent features within the target domain. Finally, the classifier is fine-tuned on few-shot tasks to adapt to specific categories of the target domain. Extensive experimental results exhibit impressive performance of DMPC on 12 RS cross-domain scenarios.

Image generation is a crucial task to facilitate intelligent interpretation in remote sensing domain. Expanding dataset size through image generation can enhance model performance of downtown task. However, current generative models in remote sensing are mostly unconditional or guided by simple text, resulting in generated images lacking spatial and semantic constraints. This lack of control can negatively optimize downstream task models. To tackle these challenges, a two-stage controllable text-image generative model called Diffusion-Geo is presented. In the first stage, an extensive image-text generation dataset called RS-Control is created through prompt engineering of multimodal large language models (MLLMs) and manual prompts for existing datasets, incorporates diverse conditional controls with rich spatial and semantic information. Then RS-Control dataset is utilized to train a universal controllable image generative model. The second stage involves efficient tuning the universal model for different task datasets, minimizing fine-tuning costs while preserving diversity and high-quality features. Experiments conducted on the RSICD caption dataset and WHU change detection dataset demonstrate the superiority of Diffusion-Geo over other state-of-the-art models in image generation.

Cross-domain few-shot scene classification (CDFSSC) is crucial for remote sensing (RS) applications since it aims at transferring knowledge learned from the source domain to the target domain to facilitate the model’s few-shot classification for the target domain. However, existing methods ignored the feature statistic discrepancy caused by domain shifts, leading to an inferior performance on the target domain. In this paper, to facilitate the model’s adaptation of the domain shifts and achieve better cross-domain knowledge transfer, an uncertainty-injected cross-domain framework called UICD is proposed for CDFSSC tasks from RS imagery. First, a semi-supervised teacher-student structure is employed to achieve cross-domain knowledge transfer by conducting supervised learning on labeled source data and establishing consistent predictions on unlabeled target data. Secondly, uncertainty is injected in feature statistic modeling during cross-domain training to obtain more diverse feature statistics for data from both the source and target domains, which could promote the robustness and adaptation of the model to domain shifts, thus enabling the model to better adapt to unforeseen variations in the target domain. Extensive experiment results indicate the efficacy and superiority of the proposed methods.

In the realm of remote sensing, targets of interest span a range of categories. However, their distribution is not always uniform. Certain categories substantially outnumber others, resulting in what’s termed a ‘long-tailed distribution’ in remote sensing imagery. This imbalanced distribution often biases a classifier’s focus toward the more abundant (head) classes, at the detriment of the less-represented (tail) classes. Such biases undermine the classifier’s generalization performance, particularly in the context of remote sensing image classification (RSIC). While existing mitigation approaches such as resampling, reweighting, and transfer learning offer some respite, they often miss out on in-depth knowledge refinement, rendering them less effective for severe long-tailed RSIC scenarios. To counter these challenges, we introduce DECOR, a dynamic decoupling and multi-objective optimization framework. Within DECOR, the feature extractor and classifier are dynamically decoupled, promoting superior feature representation and classifier training. Then, a multi-objective optimization approach is proposed to delve deeper, refining feature representation at the knowledge level using learnable feature centroids coupled with masked world knowledge learning. Moreover, to combat the pronounced effects of sample imbalance on classifier training, we employ a class-balanced re-sampling technique paired with a parameter-efficient adapter, which sharpens the classifier’s decision boundary and bridges the gap between representation and classification. DECOR’s efficacy is validated through comprehensive experiments on several datasets, including the NWPU-RESISC45-LT (NWPU-LT), AID-LT, and our self-built BIT-AFGR50-LT. Experimental results demonstrate DECOR’s marked enhancement in performance on long-tailed datasets. Our source code is available at: https://github.com/ChloeeGrace/DECOR.

Cross-domain few-shot scene classification (CDFSSC) is devoted to transferring knowledge from the source domain to the target domain and facilitating few-shot classification for the target domain. However, due to the domain shifts between source and target domains, high uncertainty would be generated in the knowledge transfer process, leading to unreliable cross-domain learning, which degenerates classification performance on the target domain severely. Thus, in this paper, aiming to reduce the interference of high uncertainty and improve the reliability of cross-domain knowledge transfer, a novel uncertainty-aware dynamic learning (UDL) framework is proposed for CDFSSC from remote sensing imagery. First, a mean-teacher architecture combining pseudo-labeling and consistency regularization is utilized to achieve cross-domain learning. Second, a UDL strategy is proposed to divide data into positive and negative samples based on a well-designed uncertainty-aware dynamic threshold, conducting positive and negative learning respectively, to advance a more reliable knowledge transfer. Third, to further improve cross-domain capability, a self-entropy loss is designed to reduce the epistemic uncertainty of the model. Extensive experiment results indicate the superiority of our proposed methods.

Change detection (CD) is an essential task in optical remote sensing, and it can be used to extract the valid information from sequential multitemporal images. However, since the character of long-term revisiting and very high resolution (VHR) development, the great differences of illumination, season, and interior textures between bitemporal images bring considerable challenges for pixel-wise CD. In this letter, focusing on accurate pixel-wise CD, a bilateral semantic fusion Siamese network (BSFNet) is proposed. First, to better map bitemporal images into semantic feature domain for comparison, a novel BSFNet is designed to effectively integrate shallow and deep semantic features, which can provide pixel-wise CD results with complete regions and clear boundary locations. Then, in order to facilitate the reasonable convergence of the proposed BSFNet, a scale-invariant sample balance (SISB) loss is designed for metric learning to avoid the problems of sample imbalance and scale variance. Finally, extensive experiments are carried out on two published CDD and LEVIR CD datasets, and results indicate that the proposed BSFNet can provide superior performance than the other state-of-the-art methods. Our work is available athttps://github.com/ClarissaDHL/BSFNet.

Scene classification of optical remote sensing is a basic but important task because of its broad application in a range of fields. Due to the powerful feature extraction capabilities, convolutional neural networks (CNNs) have been widely used in optical remote sensing scene classification tasks. Despite the remarkable efforts have been achieved, there are still several problems existed, including the effective multiscale feature description for complex scene, rotation, and low interclass diversity problems. In this letter, to address these mentioned problems and construct a powerful CNN for optical remote sensing scene classification, an effective multiscale residual network with a high-order feature representation (MRHNet) is proposed. First, data preprocessing is utilized to adapt the rotation invariance problem. Second, related to the original residual module, a pyramid convolution is introduced to realize the multiscale feature extraction, and then, its feature description ability is further improved by an effective channel attention module. Third, inspired by the tensor decomposition and its completion, a high-order feature representation structure is designed for recovering discriminative fine-scale details into deep layers to solve the low interclass diversity problem. Finally, extensive experiments are carried on two widely used scene classification datasets (e.g., AID and NWPU-RESISC45), and comparing results show that the proposed MRHNet can achieve superior performances.