Aug 2026· Remote Sensing· 0 citations· 53 references
TL;DR
A three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training, Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM) is proposed, demonstrating the effectiveness and generalizability of the proposed framework across diverse remote sensing scenarios under image-level supervision.
Abstract
Pixel-level annotation of remote sensing imagery is costly, motivating weakly supervised semantic segmentation (WSSS) using only image-level labels. However, class activation maps (CAMs) often highlight only discriminative sub-regions and fail to separate adjacent land-cover regions, particularly in remote sensing scenes characterized by densely co-occurring land-cover classes and substantial variations in object scale. To address these limitations, we propose a three-stage framework that integrates complementary priors from Contrastive Language–Image Pre-training (CLIP), Self-Distillation with No Labels version 2 (DINOv2), and the Segment Anything Model (SAM). First, a lightweight CLIP adapter aligns vision–language priors with remote sensing imagery, while sigmoid-based multi-label decoupled distillation replaces class-competitive distillation with independent class-wise supervision, producing more complete CAMs. Second, DINOv2-guided feature clustering decomposes large merged regions before SAM prompt generation, while Spatial–Semantic Constraints are used to construct confidence-guided point-and-box prompts and reject excessively expanded or semantically inconsistent masks, thereby generating reliable pseudo-labels. Finally, a compact segmentation network is initialized with the weights learned in Stage 1 and retrained using the refined pseudo-labels generated in Stage 2, eliminating the need for foundation models during inference. Experiments on the Potsdam, LoveDA, and DeepGlobe datasets show that the proposed method achieves mean intersection over union (mIoU) scores of 53.16%, 52.66%, and 62.98%, respectively, outperforming state-of-the-art WSSS baselines by 6.55, 1.16, and 1.27 percentage points, respectively. These results demonstrate the effectiveness and generalizability of the proposed framework across diverse remote sensing scenarios under image-level supervision.
Multimodal fusion methods have shown great potential in remote sensing image analysis, but existing approaches rely heavily on massive amounts of annotated data. This is not only costly and time-consuming but also prone to subjective bias. To address this issue, we propose a category-prior-based self-supervised framework, CGSNet, which uses category prior maps extracted from multispectral images as supervisory signals for end-to-end training. An adaptive confidence-weighted pseudo-label generation mechanism is designed to alleviate noise and errors in prior maps by replacing binary labels with continuous confidence maps, enabling the learning of uncertain interclass features. In addition, a multispectral feature-guided refinement strategy utilizes color and texture information to calibrate class transition regions and enhance the discriminative power of pseudo-labels in complex scenes. A dynamic mask selection strategy further enhances the model’s robustness and generalization capabilities through progressive learning. Experiments demonstrate that CGSNet achieves state-of-the-art performance without the need for human annotation, achieving an Mean Intersection over Union (mIoU) score of 78.46% on the Gaofen image dataset (GID) (vegetation) dataset and 79.58% on the Zurich (vegetation) dataset—12.22% and 15.07% higher than existing methods, respectively—while exhibiting strong cross-dataset zero-shot generalization capabilities. The code will be available at https://github.com/NUAALISILab
Jiahang Liu, Jian Cui, Mao-yin Guo et al.· IEEE Transactions on Geoscie...· 0 citations
Referring remote sensing image segmentation (RRSIS) aims to segment target ground objects in high-resolution remote sensing imagery according to textual descriptions. Due to complex backgrounds and large-scale variations, accurately aligning linguistic semantics with spatial regions remains a challenging problem. Existing RRSIS methods are predominantly segmentation-centric, where textual descriptions mainly serve as conditional guidance for pixel-wise prediction rather than explicitly enforcing semantic consistency between visual regions and linguistic representations. This limitation often leads to suboptimal fine-grained alignment and insufficient suppression of background-dominant regions. To address these challenges, we propose a novel RRSIS framework centered on structure-constrained semantic contrastive learning (CL), termed S2CLNet. Departing from the segmentation-driven paradigm, instead of relying solely on segmentation supervision, S2CL introduces region-level structural constraints to perform fine-grained CL in the joint embedding space. This design encourages semantically corresponding visual regions and linguistic representations to be more closely aligned while effectively separating target regions from irrelevant background areas. Furthermore, we develop a dynamic modality balancing module (DMBM) to enhance cross-modal interaction under complex remote sensing scenarios. The DMBM jointly models bidirectional cross-modal attention (BCA) and dynamically adjusts the relative contributions of visual and linguistic modalities according to scene complexity and linguistic specificity, thereby facilitating more adaptive and robust multimodal feature integration. Extensive experiments on three public benchmarks, RefSegRS, RRSIS-D, and RISBench, demonstrate that the proposed method achieves superior performance compared with state-of-the-art approaches. The code will be publicly available at https://github.com/45degreesl
Sen Lei, Shuai Li, Xin-Yu Xiao et al.· IEEE Transactions on Geoscie...· 0 citations
Image-level weakly supervised remote sensing semantic segmentation aims to learn pixel-level land-cover prediction using only image-level labels, greatly reducing the annotation cost of fully supervised methods. Class activation map (CAM)-based methods are widely used for this task, but they usually focus on the most discriminative regions, leading to incomplete activation and inaccurate boundaries. Recently, vision Transformer (ViT)-based methods have been introduced to alleviate the limitation of CAMs by exploiting token relations and attention mechanisms. However, remote sensing images often contain dense land-cover regions with subtle interclass differences, and patch tokens in deep ViT layers may become oversmoothed without explicit patch-level supervision, weakening local semantic discrimination. Moreover, large intraclass variations and frequent category co-occurrence make image-specific class tokens prone to semantic drift across different remote sensing images. To address these problems, we propose a global and neighbor-aware token learning (GNATL) framework. GNATL contains two complementary modules: neighbor-aware patch token learning (NPTL) and global class token learning (GCTL). NPTL exploits overlapping regions between neighboring crops to construct implicit patch-level constraints, thereby alleviating patch token oversmoothing. Global class token learning (GCTL) dynamically maintains global class tokens as category-level prototypes to guide image-specific class tokens toward stable category semantics. Experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam, ISPRS Vaihingen, and DeepGlobe Land Cover datasets show that GNATL achieves mean Intersection over Union (mIoU) scores of 56.38%, 47.74%, and 62.75%, outperforming the best compared methods by 2.63%, 4.57%, and 2.36%, respectively.
Mansu Gu, Jing Bai, Rui-Zhe Guan et al.· IEEE Transactions on Geoscie...· 0 citations
CROSS is proposed, a tightly integrated paradigm for RRSIS that achieves state-of-the-art performance and maintains precise localization even under severe spatial description perturbations, standing as a robust new paradigm for RRSIS.
Tingzhang Luo, Ruizhong Liu, Yichao Liu et al.· 1 citation
Semantic segmentation of high-resolution remote sensing images remains challenging due to complex spatial structures, multiscale object variations, fine-grained category differences, and high interclass similarities. Conventional segmentation methods usually rely on fixed convolutional heads or single feature representations, which makes it difficult to effectively model both intraclass appearance variations and interclass texture similarities, often leading to category confusion, missed objects, and incomplete segmentation in complex scenes. To address these challenges, we propose a state-aware prototype learning network, termed SAPLNet. Specifically, a cross-stage state refiner is introduced to progressively refine multilevel features by integrating the input features with the outputs of different stages through state-aware gated normalization. Then, a weighted feature pyramid decoder performs top-down fusion of the refined hierarchical features, combining high-level semantic information with low-level spatial details. Furthermore, a state-aware multiprototype classifier is designed to construct multiple semantic prototypes for each class via ground-truth-guided local class-center extraction and momentum-based prototype memory updating. A global state vector derived from the refined cross-stage features is used to adaptively modulate decoder features, improving the matching reliability between pixel features and class prototypes. In addition, prototype compactness loss, prototype diversity loss, and lightweight boundary loss are employed to enhance intraclass consistency, prototype discriminability, and boundary awareness. Experimental results demonstrate the effectiveness and superiority of SAPLNet.
Zeyu Zhao, Zhaolong Gao, Jun Feng· IEEE Journal of Selected Top...· 0 citations
High-precision, pixel-level annotations are indispensable for remote sensing semantic segmentation and related tasks, yet producing such labels manually is prohibitively expensive. Although recent generative models can synthesize realistic remote sensing data, existing approaches typically either rely heavily on preexisting ground-truth masks as conditioning inputs or lack precise control over the spatial layout of the generated content. To address this gap, we propose Graph2Scene, a novel framework for the joint generation of remote sensing images and pixel-level labels driven by scene graphs. This framework establishes a flexible control mechanism that utilizes scene graphs derived from existing semantic labels during training to learn semantic priors, while enabling users to explicitly define object quantities, categories, and topological relationships for customized generation during inference. Graph2Scene adopts a two-stage cascade: Graph2Mask encodes the scene graph into textual prompts and employs an image-level low-rank adaptation (LoRA) to finetune a FLUX model for label generation; Mask2Scene uses a class-level LoRA strategy to learn fine-grained visual features and generates remote sensing images conditioned on the label. Experiments on our developed non-agricultural conversion process (NACP) dataset and the public LoveDA dataset show that Graph2Scene effectively produces structurally coherent and visually realistic remote sensing images together with accurate pixel-level annotations. The code will be made available at https://github.com/GeoRSAI/Graph2Scene
Shaoxuan Zhao, Xiaoguang Zhou, Dongyang Hou et al.· IEEE Geoscience and Remote S...· 0 citations
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