With the development of deep learning, change detection technology has gained great progress. However, how to effectively extract multi-scale substantive changed features and accurately detect small changed objects as well as the accurate details is still a challenge. To solve the problem, we propose Attentived Differential High-Resolution Change Detection Network (ADHR-CDNet) for remote sensing images. In ADHR-CDNet, a novel high-resolution backbone with a Differential Pyramid Module (DPM) is proposed to extract multi-level and multi-scale substantive changed features. The backbone structure with four interconnected sub-network branches of different resolution is helpful to extract multi-level and multi-scale features. DPM is capable of distinguishing between substantive changes and pseudo changes induced by illumination, shadow, seasonal variation, and so on. Then, a novel Multi-Scale Spatial feature Attention Module (MSSAM) is presented to effectively fuse the spatial detail information of different scale features produced by our backbone to generate finer prediction. We conduct quantitative and qualitative experiments on three public change detection datasets: the Lebedev, the LEVIR-CD, and the WHU Building dataset. The proposed ADHR-CDNet reaches F1-score of 97.2% (improved 3.1%) on the Lebedev dataset, 91.4% (improved 1.6%) on the LEVIR-CD dataset, and 90.9% (improved 1.2%) on the WHU Building dataset. The experimental results demonstrate that our method performs much better than the state-of-the-art methods. The visualization comparison results show that our method can effectively detect small changed objects and significantly improve the details of detected changed objects. Our code is available at https://github.com/w-here/ASGO-113lab/tree/main/ADHR-CDNet.

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.

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 imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in recent years have transferred the DCNN-based model to remote sensing data analysis. However, the wide-range observation areas, complex and diverse objects and illumination and imaging angle influence the pixels easily confused, leading to undesirable results. Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions. HCANet hybridizes cross-level contextual and attentive representations to emphasize the distinguishability of learned features. First of all, a cross-level contextual representation module (CCRM) is devised to exploit and harness the superpixel contextual information. Moreover, a hybrid representation enhancement module (HREM) is designed to fuse cross-level contextual and self-attentive representations flexibly. Furthermore, the decoder incorporates DUpsampling operation to boost the efficiency losslessly. The extensive experiments are implemented on the Vaihingen and Potsdam benchmarks. In addition, the results indicate that HCANet achieves excellent performance on overall accuracy and mean intersection over union. In addition, the ablation study further verifies the superiority of CCRM.

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.

Attention mechanisms have revolutionized the semantic segmentation network in interpreting remotely sensed images (RSIs) due to their amazing ability in establishing contextual dependencies. Nevertheless, due to the complex scenes and diverse objects in RSIs, a variety of details and correlations are not available in Euclidean space. Therefore, a similarity-hybrid attention module (SHAM) is devised to attentively learn the hyperbolic and Euclidean attention maps between any two positions, followed by a weighted element-wise summation. The hybrid attention maps posses latent geometric properties of both Euclidean and hyperboloid. Taking commonly-used fully convolutional network (FCN) as baseline, HAENet that embeds SHAM, is presented. Experiments on ISPRS Potsdam and DeepGlobe benchmarks reveal its superiority to comparative methods. In addition, the ablation study validates the effectiveness of SHAM compared to other attention modules.