An urban functional zone (UFZ) serves as the planning and implementation unit in urban development and management strategies. Previous works on multi-modal UFZ representation learning have integrated socio-economic attributes from points-of-interest (POIs) with visual features from remote sensing images. However, the inherent sampling bias and spatial inequality in POIs can impede the model’s discriminative capacity. To address the problems of insufficient data coverage and incomplete representation of physical-semantic sensing, we propose an interconnected, consistent and scalable framework within the physical-spatial-semantic representation space that we term TUF-Sensing. TUF-Sensing models building footprints and POIs as graph nodes, respectively, and applies a symmetrical graph convolutional architecture to capture the topology of the constructed graph, and the neighborhood influence between entities. To enhance the expressivity of nodes and stimulate neighborhood aggregation, the input features of buildings and POIs are constructed differently, using the polygonal attributes of buildings and one-hot encoding that reflects the categorical identity of POIs. The conducted experiments compared the performance of TUF-Sensing and six other methods on different scales of grids and blocks in Wuhan, China. The results demonstrate that TUF-Sensing yields significant improvements in both probability distribution- and categorical performance-based metrics, indicating its adaptability in large-scale and fine-grained UFZ recognition.

Modern deep neural networks (DNNs) use supervised learning to model the probability that a sample belongs to its label (termed positive learning), achieving human-level performance in image classification tasks. However, when encountering noisy labels, this process can lead the network to fit erroneous information, diminishing its generalization capability. Scene classification in high-resolution remote sensing images (SC-HRSI) holds considerable potential for diverse applications, also has the challenge of noisy labels when collecting large-scale datasets. Negative learning (NL), where DNNs are trained using complementary labels of samples, is noise tolerant, but often struggles to achieve adequate learning performance on practical challenging datasets. In this letter, we develop an enhanced NL (ENL) module utilizes the underlying sample distribution to refine model learning in the presence of mislabeled data, improving robustness against noise. Concurrently, we propose a selective positive learning (SPL) module, which uses dynamic class-wise confidence scores to filter likely-to-be-clean data, guiding the network toward efficient convergence. Finally, the integrated negative and positive learning (INPL) framework combines these two modules into a unified training pipeline, enhancing the model’s generalization ability and accelerating the convergence process. Experimental results on two widely used SC-HRSI datasets demonstrate the effectiveness of the proposed method compared to state-of-the-art noise robust methods.

Accurate and regularly updated global road maps have many applications in intelligent navigation and urban planning. Deep learning algorithms have recently shown promising road extraction results using various Earth observation data. However, limitations in sample annotation and domain discrepancies between different regions and sensors pose challenges to the applicability of existing road extraction methods for large-scale tasks. In this paper, we propose a novel GlobalRoadMapper scheme to extract global-scale roads at the submeter level from optical remote sensing images that only require limited labeled samples. The proposed GlobalRoadMapper is designed as a two-stage method integrating Supervised Domain Incremental and Unsupervised Domain Adaptation. The supervised domain incremental stage learns road features from the source domain. To deal with catastrophic forgetting when deep models learn new knowledge from new domains, a strategy that couples pre-domain replay and mean-teacher is proposed. The unsupervised domain adaptation stage expands knowledge learned from the source domain to unseen scenes. Multi-level domain adaptation is proposed at the image, feature, and prediction levels in the second stage to address the issue of weak cross-domain generalization. The effectiveness of GlobalRoadMapper was tested at 43 sites worldwide. Qualitative and quantitative results demonstrate that GlobalRoadMapper outperforms the existing methods for global-scale road extraction tasks. Furthermore, city-scale sub-meter road mapping was conducted in six cities from different regions worldwide. GlobalRoadMapper achieved visual results comparable to the manually annotated OpenStreetMap road networks. Overall, GlobalRoadMapper holds great potential for large-scale road extraction and can be adapted to create easily updatable road maps globally.

Urban function mapping serves as a vital role in urban management and planning tasks. To generate fine-grained recognition at spatial and semantic scale, a contrastive manner integrating comprehensive representation from multi-modal descriptions of urban functional zones (UFZs)is proposed. Abstract physical features from VHR images are obtained from the founder deep convolutional model. Spatial pattern and semantic features are extracted from geographical entities including urban buildings and POIs, respectively. The proposed model is validated in the downtown Wuhan, China, where resources and sensing data citywide are concentrated. Rich information is provided but also the challenges due to the high complexity are posed for urban functions recognition. The superior performances demonstrate that the multidimensional, especially the integration with spatial pattern of primary urban materials, enhances the exact and robust recognition on sophisticated functions of urban land.

Satellite video multi-object tracking holds significant importance in national defense security, emergency rescue, urban planning, and various other applications. This task is particularly challenging due to the complex background of remote sensing images and the dynamic nature of spatiotemporal processes. Currently, satellite video object tracking faces issues like erroneous detection and tracking, especially in cases of low contrast and missed detection and tracking for small objects. To address these challenges, this study introduces a method for multi-object tracking in satellite videos that considers both global information and local features of objects. The proposed method also incorporates weak feature enhancement techniques, specifically targeting issues such as low contrast. Extensive experimental verification on Jilin-1 satellite video data has been conducted, yielding excellent results and demonstrating the effectiveness of the proposed approach.

Timely updated road networks are the basis for many real-world applications such as intelligent navigation and traffic management. Existing road updating methods based on remote sensing images learn from historical road databases to update roads. Road extraction models learned from historical images however, are not easily applied to a current image due to spectral differences; and only changed roads need updating. In this paper, an Unchanged-Guided Road Updating (UGRoadUpd) framework is proposed to improve the quality of updated road networks by limiting the road updating range and learning from historical unchanged roads. The UGRoadUpd framework identifies road changes using a novel dual-task dominant-transformer-based neural network for road change detection (DT-RoadCDNet). DT-RoadCDNet executes road segmentation and change detection simultaneously, from bi-temporal remote sensing images. The Dominant-Transformer based Global Context Modeling module in DT-RoadCDNet globally models the contextual spatial structure for improved integrity in roads and road changes. Based on the discovery of road changes, an unchanged-guided road update strategy updates the roads in changed areas by learning from the prior information provided by unchanged roads in a historical road database. Experiments on two newly annotated road change detection and update datasets confirms the effectiveness of our UGRoadUpd framework.

Synthetic aperture radar (SAR) image classification is a challenging subject due to the strong speckle noise present in SAR image processing. This paper is devoted to the unsupervised classification of single-band single-polarized synthetic aperture radar (SAR) images using multilayer level set approach. The principal concept is that we employ the gamma model to define the multilayer level set energy functional. The experiments conducted on both synthetic and real SAR images show that the proposed algorithm obtains improved experimental results in terms of both accuracy and efficiency.

This letter presents a novel flood detection approach using full polarimetric synthetic aperture radar (PolSAR) images based on a level set method considering prior geoinformation. The prior geoinformation includes information derived from vector data and topography data. The main approach accomplishes flood detection by the improved level set method, an active contour segmentation model, based on the classical Wishart distribution. Vector data are used to generate the zero initial level set curves. To investigate the separability between water and nonwater low-backscattering objects in PolSAR images, topography information is incorporated into the level set function as a constraint. Moreover, we introduce a piecewise statistical method to refine the result with the Kullback-Leibler divergence of circular polarization coherence. In addition, we design a new quantitative evaluation index to assess flood detection results. For validation, three real PolSAR images of flooded area are tested. The experimental results confirm the effectiveness of the proposed method.

Polarimetric features are essential to polarimetric synthetic aperture radar (PolSAR) image classification for their better physical understanding of terrain targets. The designed classifiers often achieve better performance via feature combination. However, the simply combination of polarimetric features cannot fully represent the information in PolSAR data, and the statistics of polarimetric features are not extensively studied. In this paper, we propose a joint statistical model for polarimetric features derived from the covariance matrix. The model is based on copula for multivariate distribution modeling and alpha-stable distribution for marginal probability density function estimations. We denote such model by CoAS. The proposed model has several advantages. First, the model is designed for real-valued polarimetric features, which avoids the complex matrix operations associated with the covariance and coherency matrices. Second, these features consist of amplitudes, correlation magnitudes, and phase differences between polarization channels. They efficiently encode information in PolSAR data, which lends itself to interpretability of results in the PolSAR context. Third, the CoAS model takes advantage of both copula and the alpha-stable distribution, which makes it general and flexible to construct the joint statistical model accounting for dependence between features. Finally, a supervised Markovian classification scheme based on the proposed CoAS model is presented. The classification results on several PolSAR data sets validate the efficacy of CoAS in PolSAR image modeling and classification. The proposed CoAS-based classifiers yield superior performance, especially in building areas. The overall accuracies are higher by 5%–10%, compared with other benchmark statistical model-based classification techniques.

Automatic optical-to-SAR image registration is considered as a challenging problem because of the inconsistency of radiometric and geometric properties. Feature-based methods have proven to be effective; however, common features are difficult to extract and match, and the robustness of those methods strongly depends on feature extraction results. In this paper, a new method based on iterative line extraction and Voronoi integrated spectral point matching is developed. The core idea consists of three aspects: 1) An iterative procedure that combines line segment extraction and line intersections matching is proposed to avoid registration failure caused by poor feature extraction. 2) A multilevel strategy of coarse-to-fine registration is presented. The coarse registration aims to preserve main linear structures while reducing data redundancy, thus providing robust feature matching results for fine registration. 3) Voronoi diagram is introduced into spectral point matching to further enhance the matching accuracy between two sets of line intersection. Experimental results show that the proposed method improves the matching performance. Compared with previous methods, the proposed algorithm can effectively and robustly generate sufficient reliable point pairs and provide accurate registration.