Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in $F_1$-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic representations at different levels are difficult to capture accurately at the same time, and then the extraction results decline to become suboptimal, especially on narrow areas and boundaries. To address the above-mentioned problem, a multiscale successive attention fusion network, named MSAFNet, is proposed to efficiently aggregate the multiscale features from two aspects. A successive attention fusion module (SAFM) is first devised to extract multiscale and fine-grained features of water bodies, while a joint attention module (JAM) is proposed to further mine salient semantic information by jointly modeling contextual dependencies. Furthermore, the multi-level features extracted by the above-mentioned modules are aggregated by a feature fusion module (FFM) so that the edges of water bodies are well mapped, directly improving the segmentation of various water bodies. Extensive experiments were conducted on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. Numerically, MSAFNet reached the highest accuracy on both QTPL and LoveDA datasets, including Kappa, MIoU, FWIoU, F1, and OA, outperforming several mainstream methods. Regarding the QTPL dataset, MSAFNet peaked at 99.14% and 98.97% in terms of F1 and OA. Although the LoveDA dataset is more challenging, MSAFNet retained the best performance, with F1 and OA being 97.69% and 95.87%. Additionally, visual inspections exhibited consistency with numerical evaluations.

Extracting water bodies is an important task in remote sensing imagery (RSI) interpretation. Deep convolution neural networks (DCNNs) show great potential in feature learning; they are widely used in the water body interpretation of RSI. However, the accuracy of DCNNs is still unsatisfactory due to differences in the many hetero-features of water bodies, such as spectrum, geometry, and spatial size. To address the problem mentioned above, this paper proposes a multiscale normalization attention network (MSNANet) which can accurately extract water bodies in complicated scenarios. First of all, a multiscale normalization attention (MSNA) module was designed to merge multiscale water body features and highlight feature representation. Then, an optimized atrous spatial pyramid pooling (OASPP) module was developed to refine the representation by leveraging context information, which improves segmentation performance. Furthermore, a head module (FEH) for feature enhancing was devised to realize high-level feature enhancement and reduce training time. The extensive experiments were carried out on two benchmarks: the Surface Water dataset and the Qinghai–Tibet Plateau Lake dataset. The results indicate that the proposed model outperforms current mainstream models on OA (overall accuracy), f1-score, kappa, and MIoU (mean intersection over union). Moreover, the effectiveness of the proposed modules was proven to be favorable through ablation study.

Automatically extracting water bodies is a significant task in interpreting remote sensing images (RSIs). Convolutional neural networks (CNNs) have exhibited excellent performance in processing RSIs, which have been widely used for fine-grained extraction of water bodies. However, it is difficult for the extraction accuracy of CNNs to satisfy the requirements in practice due to the limited receptive field and the gradually reduced spatial size during the encoder stage. In complicated scenarios, in particular, the existing methods perform even worse. To address this problem, a novel boundary-guided semantic context network (BGSNet) is proposed to accurately extract water bodies via leveraging boundary features to guide the integration of semantic context. Firstly, a boundary refinement (BR) module is proposed to preserve sufficient boundary distributions from shallow layer features. In addition, abstract semantic information of deep layers is also captured by a semantic context fusion (SCF) module. Based on the results obtained from the aforementioned modules, a boundary-guided semantic context (BGS) module is devised to aggregate semantic context information along the boundaries, thereby enhancing intra-class consistency of water bodies. Extensive experiments were conducted on the Qinghai–Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. The results demonstrate that the proposed BGSNet outperforms the mainstream approaches in terms of OA, MIoU, F1-score, and kappa. Specifically, BGSNet achieves an OA of 98.97% on the QTPL dataset and 95.70% on the LoveDA dataset. Additionally, an ablation study was conducted to validate the efficacy of the proposed modules.

Cloud contamination significantly impairs optical remote sensing images (RSIs), reducing their utility for Earth observation. The traditional cloud removal techniques, often reliant on deep learning, generally aim for holistic image reconstruction, which may inadvertently alter the intrinsic qualities of cloud-free areas, leading to image distortions. To address this issue, we propose a multi-stage frequency attention network (MFCRNet), a progressive paradigm for optical RSI cloud removal. MFCRNet hierarchically deploys frequency cloud removal modules (FCRMs) to refine the cloud edges while preserving the original characteristics of the non-cloud regions in the frequency domain. Specifically, the FCRM begins with a frequency attention block (FAB) that transforms the features into the frequency domain, enhancing the differentiation between cloud-covered and cloud-free regions. Moreover, a non-local attention block (NAB) is employed to augment and disseminate contextual information effectively. Furthermore, we introduce a collaborative loss function that amalgamates semantic, boundary, and frequency-domain information. The experimental results on the RICE1, RICE2, and T-Cloud datasets demonstrate that MFCRNet surpasses the contemporary models, achieving superior performance in terms of mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), validating its efficacy regarding the cloud removal from optical RSIs.