Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependency on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between source domain and target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level prototype information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level memory aggregation module to current pseudo invariant feature for further augmenting the representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods.

Semantic segmentation is an important task of analysis and understanding of high-resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN)-based model shows their excellent performance in remote sensing image semantic segmentation. Most of the existing HRSI semantic segmentation methods are only designed for a very limited data domain, that is, the training and test images are from the same dataset. The accuracy drops sharply once a model trained on a certain dataset is used for cross-domain prediction due to the difference in feature distribution of the dataset. To this end, this article proposes an unsupervised domain adaptation framework based on adversarial learning for HRSI semantic segmentation. This framework uses high-level feature alignment to narrow the difference between the source and target domains at the semantic level. It uses the category-certainty attention module to reduce the attention of the classifier on category-level aligned features and increase the attention on category-level unaligned features. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in cross-domain segmentation.

When the buildings themselves or the environments they are located in change, it poses a cross-domain problem for building extraction. Since the semantics of the same category should be consistent across domains, a memory mechanism can be used to drive models to capture it. A key prerequisite for a satisfactory memory mechanism is that the memory content is highly task-relevant, i.e., the memory mechanism represents domain-invariant semantic features of buildings as much as possible. It is worth noting that images from different domains have diverse styles, which can interfere with the representation of domain-invariant semantic features of buildings. To maximize the effectiveness of memory mechanisms in cross-domain building extraction tasks, it is necessary to reduce the interference of style information. Therefore, we propose a cross-domain extraction method for buildings based on decoupling style-semantic features. It expresses the image style features and building semantic features separately and optimizes the domain-invariant features of buildings by constraining their relationship through an orthogonal loss. Specifically, on the one hand, style features within the same domain as well as semantic features within and across domains are narrowed down through contrastive learning to ensure the consistency of style features extracted within the same domain and the consistency of semantics across domains. On the other hand, the style features and domain-invariant semantic features in the memory mechanism can be stored and reused to fully exploit self-supervised information. Results of the cross-domain experiments show that the proposed method can achieve optimal building extraction.

Road extraction from high-resolution remote sensing images is a critical task in image understanding and analysis, yet it poses significant challenges because of road occlusions caused by vegetation, buildings, and shadows. Deep convolutional neural networks have emerged as the leading approach for road extraction because of their exceptional feature representation capabilities. However, existing methods often yield incomplete and disjointed road extraction results. To address this issue, we propose CR-HR-RoadNet, a novel high-resolution road extraction network that incorporates local and global context reasoning. In this work, we introduce a road-adapted high-resolution network as the feature encoder, effectively preserving intricate details of narrow roads and spatial information. To capture multi-scale local context information and model the interplay between roads and background environments, we integrate multi-scale features with residual learning in a specialized multi-scale feature representation module. Moreover, to enable efficient long-range dependencies between different dimensions and reason the correlation between various road segments, we employ a lightweight coordinate attention module as a global context-aware algorithm. Extensive quantitative and qualitative experiments on three datasets demonstrate that CR-HR-RoadNet achieves superior extraction accuracy across various road datasets, delivering road extraction results with enhanced completeness and continuity. The proposed method holds promise for advancing road extraction in challenging remote sensing scenarios and contributes to the broader field of deep-learning-based image analysis for geospatial applications.

Few-shot remote sensing scene classification (FSRSSC) has been used for new class recognition in the presence of a limited number of labeled samples. The representation vector (prototype) of categories obtained using images only confronts some challenges, such as insufficient generalization when the number of samples is too small. To address this problem, we propose a new FSRSSC method based on prototype networks, named CNSPN, which combines semantic information of class names (name of the scene categories, such as aircraft, harbor, and bridge). First, CNSPN extracts semantics for class names using a pre-trained word-embedding model, which enriches the feature representation ability of the category at the source. Then, an enhanced fusion prototype is generated by fusing the semantic information of text and visual information in the image through a multimodal prototype fusion module (MPFM). Finally, the query image is classified by measuring the distance between the query sample and the visual prototype, and between the query sample and the fusion prototype. Comparative experiments on the NWPU-RESISC45 and RSD46-WHU datasets show that the proposed method significantly improves FSRSSC performance. Code is available at https://github.com/RS-CSU/CNSPN.git.

A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of street scenes, but it is difficult for street-view data to cover every area of the built environment and its update frequency is low, which cannot meet the requirement of built-environment assessment under rapid urban development. Earth-observation data have the advantages of wide coverage, high update frequency, and good availability. This paper proposes an intelligent evaluation method for urban built environments based on scene understanding of high-resolution remote-sensing images. It contributes not only the assessment criteria for the built environment in remote-sensing images from the perspective of visual cognition but also an image-caption dataset applicable to urban-built-environment assessment. The results show that the proposed deep-learning-driven method can provide a feasible paradigm for representing high-resolution remote-sensing image scenes and large-scale urban-built-area assessment.