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Learning transferable land cover semantics for open vocabulary interactions with remote sensing images

ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2025年 220卷 621-636页

作者： Zermatten, Valerie Castillo-Navarro, Javiera Marcos, Diego Tuia, Devis Ecole Polytech Fed Lausanne Environm Computat Sci & Earth Observat Lab CH-1951 Sion Switzerland CEDRIC Conservatoire Natl Arts & Metiers F-75141 Paris France Univ Montpellier INRIA F-34090 Montpellier France

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datasets. Open vocabulary recognition, i.e. using natural language to define a specific object or pattern in an image, breaks free from predefined nomenclature and offers flexible recognition of diverse categories with amore general image understanding across datasets and labels. The open vocabulary framework opens doors to search for concepts of interest, beyond individual class boundaries. In this work, we propose to use Text As supervision for COntrastive Semantic Segmentation (TACOSS), and we design an open vocabulary semantic segmentation model that extends its capacities beyond that of a traditional model for land cover mapping: In addition to visual pattern recognition, TACOSS leverages the common sense knowledge captured by language models and is capable of interpreting the image at the pixel level, attributing semantics to each pixel and removing the constraints of a fixed set of land cover labels. By learning to match visual representations with text embeddings, TACOSS can transition smoothly from one set of labels to another and enables the interaction with remote sensing images in natural language. Our approach combines a pretrained text encoder with a visual encoder and adopts supervised contrastive learning to align the visual and textual modalities. We explore several text encoders and label representation methods and compare their abilities to encode transferable land cover semantics. The model's capacity to predict a set of different land cover labels on an unseen dataset is also explored to illustrate the generalization capacities across domains of our approach. Overall, TACOSS is a general method and permits adapting between different sets of land cover labels with minimal computational overhead. Code

关键词： Land cover mapping Open vocabulary semantic segmentation Vision-language model for remote sensing

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Lightweight remote sensing super-resolution with multi-scale graph attention network

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pattern recognition 2025年 160卷

作者： Wang, Yu Shao, Zhenfeng Lu, Tao Huang, Xiao Wang, Jiaming Zhang, Zhizheng Zuo, Xiaolong Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China Wuhan Inst Technol Sch Comp Sci & Engn Hubei Key Lab Intelligent Robot Wuhan 430073 Peoples R China Emory Univ Dept Environm Sci Atlanta GA 30322 USA

remote sensing Super-Resolution (RS-SR) constitutes a pivotal component in the domain of remote sensing image analysis, aimed at enhancing the spatial resolution of low-resolution imagery. Recent advancements have seen deep learning techniques achieving substantial progress in the RS-SR field. Notably, Graph Neural Networks (GNNs) have emerged as a potent mechanism for processing remote sensing images, adept at elucidating the intricate inter-pixel relationships within images. Nevertheless, a prevalent limitation among existing GNN-based methodologies is their disregard for the high computational demands, which circumscribes their applicability in environments with limited computational resources. This paper introduces a streamlined RS-SR framework, leveraging a Multi-Scale Graph Attention Network (MSGAN), designed to effectively balance computational efficiency with high performance. The core of MSGAN is a novel multi-scale graph attention module, integrating graph attention block and multi-scale lattice block structures, engineered to comprehensively assimilate both localized and extensive spatial information in remote sensing images. This enhances the framework's overall efficacy and resilience in RS-SR tasks. Comparative experimental analyses demonstrate that MSGAN delivers competitive results against state-of-the-art methods while reducing parameter count and computational overhead, presenting a promising avenue for deployment in scenarios with limited computational resources.

关键词： remote sensing Multi-scale network Lightweight network Super-resolution Graph attention network

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Dual-Stream Network of Vision Mamba and CNN with Auto-Scaling for remote sensing image Segmentation 7th

Dual-Stream Network of Vision Mamba and CNN with Auto-Scalin...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Song, Shitao Liu, Ye Su, Jintao Nanjing Univ Posts & Telecommun Sch Automat & Artificial Intelligence Nanjing 210003 Peoples R China

ISBN: (纸本)9789819785049;9789819785056

Vision Mamba (VMamba) has recently attracted great research attention due to its ability to obtain a global receptive field with linear computational complexity. However, similar to Vision Transformer (ViT), due to its mechanism of dividing patches, it also faces the issue of insufficient description ability of local details. To address this issue, we design in this paper a dual-stream network that combines VMamba and CNN, aiming to enable the network to possess both the global receptive field of VMamba and the local detail description capability of CNN. Both of the two characteristics are crucial for remote sensing image semantic segmentation. The two streams are supervised and trained through independent loss functions. On the other hand, to enable sufficient information exchange between the two branches, we introduce an auto-scaling fusion module aiming at bridging the semantic gap between VMamba and CNN. Experiments demonstrate that the method proposed in this paper outperforms state-of-the-art methods on multiple remote sensing semantic segmentation datasets.

关键词： Vision Mamba CNN Auto-scaling Semantic segmentation remote sensing

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Adaptive Cross-Spatial sensing Network for Change Detection 7th

Adaptive Cross-Spatial Sensing Network for Change Detection

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7th Chinese conference on pattern recognition and Computer Vision

作者： Jin, Liyuan Zhang, Yanduo Lu, Tao Wang, Jiaming Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan 430205 Peoples R China Hubei Univ Arts & Sci Xiangyang 441053 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The problem of unbalanced category distribution of foreground and background in remote sensing change detection (CD) tasks has become a key problem in improving the performance of CD models when processing complex remote sensing image data. To address this issue, we develop a novel adaptive cross-spatial sensing network (ACS-Net), aiming to dynamically adjust the degree of attention of the foreground and background to achieve accurate and efficient CD. The proposed ACS-Net combines the global context module (GCM) and the cross-spatial learning module (CSLM) by introducing the adaptive weight adjustment mechanism (AWAM) to adaptively adjust the weight coefficients of the GCM and CSLM, thereby enhancing the ability to extract foreground features. GCM captures the global context information of the image to generate global perceptual features, thereby helping to distinguish the foreground and background. While CSLM focuses on learning the information association between different spatial locations, which can enhance the model's sensitivity to spatially changing details, and then focus on local feature extraction of foreground. The experimental results show that the F1-Score index of ACS-Net on two public datasets (Levir-CD and Lebedev-CD) reaches 91.53% and 96.25% respectively, validating its effectiveness and superiority.

关键词： Change detection Deep learning remote sensing High-resolution images

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A new design of an efficient configurable circuit based on quantum-dot technology for digital image processing

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ANALOG INTEGRATED CIRCUITS AND SIGNAL processing 2025年第3期122卷 1-11页

作者： Tang, Ligang Kong, Tong Seyedi, Saied Hubei Polytech Univ Sch Elect & Elect Informat Engn Huangshi 435003 Peoples R China Daye Nonferrous Design & Res Inst Co Ltd Huangshi 435005 Peoples R China Bu Ali Sina Univ Fac Engn Dept Comp Engn Hamadan Iran

image processing is the computational manipulation and analysis of digital images, encompassing various techniques and algorithms to enhance, transform, segment, and extract meaningful information from images. It plays a vital role in various fields, including computer vision, medical imaging, remote sensing, and entertainment, enabling tasks like object recognition, image enhancement, and pattern detection by utilizing methods such as filtering, edge detection, and machine learning to process visual data and extract valuable insights. image processing circuits play a pivotal role in digital circuits, with Quantum-dot Cellular Automata (QCA) technology emerging as a viable option for nano-scale circuit implementation. While QCA presents an enticing avenue for circuit design, it has faced setbacks due to a notable prevalence of fabrication defects, thereby instigating a compelling area of exploration within QCA circuitry. Simultaneously, the significance of morphological operation circuits in digital circuits and image processing system design cannot be overstated. In this context, the present study undertakes the challenge of crafting a distinctive QCA-based morphological operations circuit that excels in area efficiency, robustness, and fault tolerance. Within the realm of QCA nano-technology, integrating fault-tolerant gates and cell redundancy emerges as a key strategy to ensure fault tolerance. A prime example is using a 5-input fault-tolerant majority gate, ingeniously harnessed to construct an efficient fault-tolerant morphological operations circuit. To materialize this endeavor, the QCADesigner-E tool takes center stage and is employed to meticulously implement the devised circuits. The outcomes of this endeavor, including evaluations of single-cell defects, quantum cost, area utilization, and delay time, unequivocally demonstrate the superiority of the designed QCA circuit compared to its predecessors. Notably, the engineered circuit boasts a minimal delay

关键词： image processing Nano Fault-tolerant Morphological operations Erosion Dilation

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Discriminative Representation-Based Classifier for Few-Shot remote sensing Classification 7th

Discriminative Representation-Based Classifier for Few-Shot ...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Yuan, Tianhao Liu, Weifeng Wang, Yingjie Liu, Baodi China Univ Petr East China Qingdao 266580 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The acquisition of sufficient labeled samples is often a significant challenge in the field of remote sensing imagery, due to the time-consuming and high cost nature of field data collection. As a result, researchers have recently aimed to explore and develop effective few-shot learning methods that can overcome the shortage of labeled data in remote sensing imagery. Few-shot learning aims to enable machine learning algorithms to learn from a few labeled data or even from a single sample. The classification of remote sensing data is challenging because of the high interclass similarity and intraclass diversity found within remote sensing scenes. Direct computation of similarities between query and support data in current methods can lead to confusion. We propose a discriminative representation-based classifier (DRC) for few-shot remote sensing scene categorization to overcome this issue. Specifically, we introduce two discriminative constraint terms in the objective function: intraclass and interclass constraints. The intraclass constraint term enhances the concentration of the learned representation vectors in same class learned by the classifier, while the interclass constraint term reduces the correlation between the representation vectors of different categories. The experimental findings on the difficult remote sensing datasets NWPU-RESISC45 and RSD46-WHU demonstrate that our proposed DRC method delivers cutting-edge results in few-shot remote sensing scene image classification.

关键词： Few-shot learning remote sensing scene classification Representation-based classifier

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Motion ship tracking method based on ship wake recognition in GF-4 remote sensing images 6

Motion ship tracking method based on ship wake recognition i...

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6th International conference on Geoscience and remote sensing Mapping, GRSM 2024

作者： Qi, Wenping Wang, Wenyu Huang, Shusong Shi, Xiaojin Li, Liutong China Centre for Resources Satellite Data and Application No. 5 Fengxian East Road Beijing100094 China

ISBN: (纸本)9781510688018

To achieve continuous monitoring and tracking of ships over a long time series, a method based on multi-temporal remote sensing images from GF-4 satellite for continuous monitoring and tracking of ship wakes is provided. This method first constructs a GF-4 satellite remote sensing dataset for ship wake recognition, performs texture enhancement processing on the remote sensing images, and finally trains the model based on the YOLOv5x network. Experimental results show that image texture enhancement can significantly improve the recognition effect, and the highest detection rate can reach 91% in cloudless and fragmented cloud environments. Through testing, the single-band imaging mode can be used for continuous tracking and monitoring of multiple targets in the same area, obtaining motion information such as target position, heading, speed, and trajectory. © 2025 SPIE.

关键词： image texture

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SFFAFormer: An Semantic Fusion and Feature Accumulation Approach for remote sensing image Change Detection 7th

SFFAFormer: An Semantic Fusion and Feature Accumulation Appr...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Hong, Yile Liu, Xiangfu Chen, Mingwei Pang, Yan Huang, Teng Wei, Bo Lang, Aobo Zhang, Xi Guangzhou Univ Inst Artificial Intelligence Guangzhou Peoples R China Beihang Univ Beijing Peoples R China Sun Yat Sen Univ Sch Art Guangzhou Peoples R China

ISBN: (纸本)9789819784929;9789819784936

The change detection task of remote sensing images provides an effective means and technology to detect changes on the Earth's surface, providing data support for disaster management. Although current methods mostly adopt hierarchical structures and variations of transformer-base models, they overlook the rich detailed features provided by shallow layers during the restoration process, as well as the accurate global features of deep layers, leading to the loss of edge details in the final change detection structure. As a solution to this problem, we suggest SFFAFormer, which employs a module design with enhanced channel learning in shallow layers to enhance edge details and feature transfer, and utilizes transformer-base modules with semantic accumulation computation in deep layers to ensure the accuracy of global information. Experimental results demonstrate that SFFAFormer surpasses many leading baselines and achieves outstanding performance on the LEVIR-CD and DSIFN-CD datasets.

关键词： Change Detection remote sensing Semantic Fusion

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Hyperspectral image Change Detection via Cross-Sample Slot Attention and Dual Gated Feed-Forward Network 7th

Hyperspectral Image Change Detection via Cross-Sample Slot A...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Cheng, Luyao Lin, Junyan Gao, Feng Ocean Univ China Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9789819784929;9789819784936

Hyperspectral image (HSI) change detection is a key research topic in the field of remote sensing. Existing HSI change detection methods often overlook the potential interactions among training samples. To address this issue, we develop a novel HSI change detection network, the Cross-Sample Slot attention-based Network (CSSNet). This network, building on the slot attention mechanism, can explicitly distinguish between changed and unchanged region representations and disentangle these representations by multiple independent concepts. These concepts are instrumental in capturing the uniformity and diversity in the representations among different samples during batch processing. Furthermore, we introduced a Dual Gated Feed-forward Network (DGFN) to effectively filter out redundant and irrelevant information. Experimental results on two different HSI datasets demonstrate that CSSNet outperforms several existing mainstream methods in performance.

关键词： Hyperspectral image Change Detection Cross-Sample Slot Attention

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DICMNet: Dynamic Irregular Resnet with Multi-direction Channel Remapping for remote sensing Road Extraction 7th

DICMNet: Dynamic Irregular Resnet with Multi-direction Chann...

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7th Chinese conference on pattern recognition and Computer Vision

作者： Li, Bowen Xiao, Feng Liu, Siyu Shen, Chao Xian Technol Univ Sch Comp Sci & Engn Xian 710021 Peoples R China Xian Technol Univ Sch Weap Sci & Technol Xian 710021 Peoples R China

ISBN: (纸本)9789819784929;9789819784936

Road information construction is the key support to solve urban traffic problems. The technique of segmenting the road part from the image is called road extraction. Therefore, road extraction can be used as a technical support for traffic system to obtain a large amount of road information. Due to the advantages of high resolution and overhead shooting angle for remote sensing image, it is very suitable as the data source of road extraction. Although road extraction from remote sensing images becomes one of the important research directions and some achievements have been made, there are still many challenges. Different from common objects, roads have strong topological continuity and are also composed of many irregular curves. This special morphological feature is difficult to be mined by common convolution kernel. To solve the above problems, this paper proposes a novel feature extraction backbone network DICMNet, which is composed of dynamic irregular convolutional (DIC) module and multi-directional channel remapping (MDCR) combine with resnet. Experimental validation with the other methods is performed on the DeepGlobe road dataset. Our model has a significant improvement on most of the evaluation metrics with an average improvement of 2%.

关键词： remote sensing road extraction dynamic irregular convolution multi-direction channel remapping

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