In remotely sensed images, high intra-class variance and inter-class similarity are ubiquitous due to complex scenes and objects with multivariate features, making semantic segmentation a challenging task. Deep convolutional neural networks can solve this problem by modelling the context of features and improving their discriminability. However, current learning paradigms model the feature affinity in spatial dimension and channel dimension separately and then fuse them in a sequential or parallel manner, leading to suboptimal performance. In this study, we first analyze this problem practically and summarize it as attention bias that reduces the capability of network in distinguishing weak and discretely distributed objects from wide-range objects with internal connectivity, when modeled only in spatial or channel domain. To jointly model both spatial and channel affinity, we design a synergistic attention module (SAM), which allows for channel-wise affinity extraction while preserving spatial details. In addition, we propose a synergistic attention perception neural network (SAPNet) for the semantic segmentation of remote sensing images. The hierarchical-embedded synergistic attention perception module aggregates SAM-refined features and decoded features. As a result, SAPNet enriches inference clues with desired spatial and channel details. Experiments on three benchmark datasets show that SAPNet is competitive in accuracy and adaptability compared with state-of-the-art methods. The experiments also validate the hypothesis of attention bias and the efficiency of SAM.

Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The emergence of the transformer intrigues the revolution of vision tasks with its impressive scalability in establishing long-range dependencies. However, the local patterns, such as inherent structures and spatial details, are broken with the tokenization of the transformer. Therefore, the ICTNet is devised to confront the deficiencies mentioned above. Principally, ICTNet inherits the encoder–decoder architecture. First of all, Swin Transformer blocks (STBs) and convolution blocks (CBs) are deployed and interlaced, accompanied by encoded feature aggregation modules (EFAs) in the encoder stage. This design allows the network to learn the local patterns and distant dependencies and their interactions simultaneously. Moreover, multiple DUpsamplings (DUPs) followed by decoded feature aggregation modules (DFAs) form the decoder of ICTNet. Specifically, the transformation and upsampling loss are shrunken while recovering features. Together with the devised encoder and decoder, the well-rounded context is captured and contributes to the inference most. Extensive experiments are conducted on the ISPRS Vaihingen, Potsdam and DeepGlobe benchmarks. Quantitative and qualitative evaluations exhibit the competitive performance of ICTNet compared to mainstream and state-of-the-art methods. Additionally, the ablation study of DFA and DUP is implemented to validate the effects.

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.

Semantic segmentation of high-resolution remote sensing images (HRRSIs) presents unique challenges due to the intricate spatial and spectral characteristics of these images. Traditional methods often prioritize spatial information while underutilizing the rich spectral context, leading to limited feature discrimination capabilities. To address these issues, we propose a novel frequency attention-enhanced network (FAENet), which incorporates a frequency attention model (FreqA) to jointly model spectral and spatial contexts. FreqA leverages discrete wavelet transformation (DWT) to decompose input images into distinct frequency components, followed by a two-stage attention mechanism comprising inner-component channel attention (ICCA) and cross-component channel attention (CCCA). These mechanisms enhance spectral representation, which is further refined through a self-attention (SA) module to capture long-range dependencies before transforming back into the spatial domain. FAENet’s encoder–decoder architecture facilitates multiscale feature refinement, enabling effective segmentation. Extensive experiments on the ISPRS Potsdam and LoveDA benchmarks demonstrate that FAENet outperforms state-of-the-art models, achieving superior segmentation accuracy. Ablation studies further validate the contributions of ICCA and CCCA. Moreover, efficiency comparisons confirm the superiority of the proposed FAENet over other models.