Semantic segmentation plays a significant role in parsing remote sensing images. However, mainstream segmentation models lack a thorough understanding of the complex structures and scale differences, and struggle to effectively locate and emphasize diverse edges. Aiming at these limitations, we propose a frequency-driven edge guidance network, named FDEG-Net, for semantic segmentation of remote sensing images. First, we design a joint sparse context aggregation module that integrates both dense local context and sparse long-range context to improve the analysis of intricate and multiscale objects. Second, an edge guidance module is developed for strong interclass edge acquisition. It applies a 2-D discrete wavelet transform, coefficient superposition method, and adaptive edge feature enhancement algorithm to reduce low-frequency information and highlight salient boundaries in spatial features. This module has two significant advantages. 1) The edge positions are defined in pixel intensity with high interpretability. 2) The modular design without additional edge labels is plug-and-play. The effectiveness and robustness of this module are validated through edge visualization results. The proposed FDEG-Net is evaluated on the Potsdam, Vaihingen, and GID datasets, demonstrating its excellent performance in accurately capturing the rich semantics of geographic space features.

Change detection (CD) is a significant branch of remote sensing image analysis. Its main distinction from general semantic segmentation lies in the input of bitemporal images. Recent CD methods have primarily focused on Euclidean space, disregarding the hidden non-Euclidean details due to the high imaging altitude and complex scenes in remote sensing imagery. This limitation hampers the model's performance. In recent years, hyperbolic space has gradually been introduced into deep learning as a classical non-Euclidean space. To explore the potential impact of hyperbolic information in CD, a hyperbolic-embedding-based Siamese network is proposed in this article. Specifically, we propose a hyperbolic similarity attention mechanism that can map deep features from bitemporal images into hyperbolic space, then establish the relationship between bitemporal features based on the hyperbolic distance, fully mining non-Euclidean information, and fusing features from both branches to enhance feature cohesiveness. Furthermore, we design a differential feature enhanced module in the decoder, which utilizes differential operations at multiple scales to highlight the essence of each layer's features and improve feature richness. Experimental results on two public very-high-resolution CD datasets demonstrate that the proposed network achieves better detection accuracy than other state-of-the-art CD methods.

Accurate lesion segmentation in medical imaging is critical for medical diagnosis and treatment. Lesions' diverse and heterogeneous characteristics often present a distinct long-tail distribution, posing difficulties for automatic methods. Currently, interactive segmentation approaches have shown promise in improving accuracy, but still struggle to deal with tail features. This triggers a demand of effective utilizing strategies of user interaction. To this end, we propose a novel point-based interactive segmentation model called Clustering-Aided Interactive Segmentation Network (CAISeg) in 3D medical imaging. A customized Interaction-Guided Module (IGM) adopts the concept of clustering to capture features that are semantically similar to interaction points. These clustered features are then mapped to the head regions of the prompted category to facilitate more precise classification. Meanwhile, we put forward a Focus Guided Loss function to grant the network an inductive bias towards user interaction through assigning higher weights to voxels closer to the prompted points, thereby improving the responsiveness efficiency to user guidance. Evaluation across brain tumor, colon cancer, lung cancer, and pancreas cancer segmentation tasks show CAISeg's superiority over the state-of-the-art methods. It outperforms the fully automated segmentation models in accuracy, and achieves results comparable to or better than those of the leading point-based interactive methods while requiring fewer prompt points. Furthermore, we discover that CAISeg possesses good interpretability at various stages, which endows CAISeg with potential clinical application value.

Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning.