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学者姓名：李蒙蒙
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Abstract ：
Heterogeneous change detection is a task of considerable practical importance and significant challenge in remote sensing. Heterogeneous change detection involves identifying change areas using remote sensing images obtained from different sensors or imaging conditions. Recently, research has focused on feature space translation methods based on deep learning technology for heterogeneous images. However, these types of methods often lead to the loss of original image information, and the translated features cannot be efficiently compared, further limiting the accuracy of change detection. For these issues, we propose a cross-modal feature interaction network (CMFINet). Specifically, CMFINet introduces a cross-modal interaction module (CMIM), which facilitates the interaction between heterogeneous features through attention exchange. This approach promotes consistent representation of heterogeneous features while preserving image characteristics. Additionally, we design a differential feature extraction module (DFEM) to enhance the extraction of true change features from spatial and channel dimensions, facilitating efficient comparison after feature interaction. Extensive experiments conducted on the California, Toulouse, and Wuhan datasets demonstrate that CMFINet outperforms eight existing methods in identifying change areas in different scenes from multimodal images. Compared to the existing methods applied to the three datasets, CMFINet achieved the highest F1 scores of 83.93%, 75.65%, and 95.42%, and the highest mIoU values of 85.38%, 78.34%, and 94.87%, respectively. The results demonstrate the effectiveness and applicability of CMFINet in heterogeneous change detection.
Keyword ：
attention mechanisms attention mechanisms Change detection Change detection CNN CNN feature interaction feature interaction heterogeneous remote sensing images heterogeneous remote sensing images
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| GB/T 7714 | Yang, Zhiwei , Wang, Xiaoqin , Lin, Haihan et al. Cross-modal feature interaction network for heterogeneous change detection [J]. | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
| MLA | Yang, Zhiwei et al. "Cross-modal feature interaction network for heterogeneous change detection" . | GEO-SPATIAL INFORMATION SCIENCE (2025) . |
| APA | Yang, Zhiwei , Wang, Xiaoqin , Lin, Haihan , Li, Mengmeng , Lin, Mengjing . Cross-modal feature interaction network for heterogeneous change detection . | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
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Building extraction from very high-resolution remote-sensing images still faces two main issues: (1) small buildings are severely omitted and the extracted building shapes have a low consistency with ground truths. (2) supervised deep-learning methods have poor performance in few-shot scenarios, limiting the practical application of these methods. To address the first issue, we propose an asymmetric Siamese multitask network integrating adversarial edge learning called ASMBR-Net for building extraction. It contains an efficient asymmetric Siamese feature extractor comprising pre-trained backbones of convolutional neural networks and Transformers under pre-training and fine-tuning paradigms. This extractor balances the local and global feature representation and reduces training costs. Adversarial edge-learning technology automatically integrates edge constraints and strengthens the modeling ability of small and complex building-shaped patterns. Aiming to overcome the second issue, we introduce a self-training framework and design an instance transfer strategy to generate reliable pseudo-samples. We examined the proposed method on the WHU and Massachusetts (MA) datasets and a self-constructed Dongying (DY) dataset, comparing it with state-of-the-art methods. The experimental results show that our method achieves the highest F1-score of 96.06%, 86.90%, and 84.98% on the WHU, MA, and DY datasets, respectively. Ablation experiments further verify the effectiveness of the proposed method. The code is available at: https://github.com/liuxuanguang/ASMBR-Net
Keyword ：
Adversarial learning Adversarial learning Building extraction Building extraction Multitask learning Multitask learning Self-learning Self-learning VHR remote-sensing image VHR remote-sensing image
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| GB/T 7714 | Liu, Xuanguang , Li, Yujie , Dai, Chenguang et al. Extraction buildings from very high-resolution images with asymmetric siamese multitask networks and adversarial edge learning [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2025 , 136 . |
| MLA | Liu, Xuanguang et al. "Extraction buildings from very high-resolution images with asymmetric siamese multitask networks and adversarial edge learning" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 136 (2025) . |
| APA | Liu, Xuanguang , Li, Yujie , Dai, Chenguang , Zhang, Zhenchao , Ding, Lei , Li, Mengmeng et al. Extraction buildings from very high-resolution images with asymmetric siamese multitask networks and adversarial edge learning . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2025 , 136 . |
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Traditional methods for detecting urban building functional types using high-resolution remote sensing images are mainly based on hand-designed features, ignoring spatial and structural features, resulting in difficulties in recognizing buildings with similar shapes. To solve this problem, this study constructs a multi-hop graph neural network (MHGNN) that integrates high-level structural and semantic information to recognize urban building functional types from high-resolution images. Experiments show that the proposed method outperforms traditional graph neural building classification methods. Our proposed method, MHGNN, demonstrates superior performance compared to standard GNNs such as MLP, GIN and GAT in multi-region building type classification tasks. The approach achieves state-of-the-art metrics, including IoU of 74.98%, F1-Score of 85.39%, and OA of 89.56%-outperforming baseline methods by significant margins. © 2025 IEEE.
Keyword ：
Buildings Buildings Classification (of information) Classification (of information) Feature extraction Feature extraction Image classification Image classification Neural networks Neural networks Remote sensing Remote sensing Satellite imagery Satellite imagery Semantics Semantics
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| GB/T 7714 | Cai, Xiaojiao , Li, Mengmeng , Zha, Ying . Building types classification using MHGNN from high-resolution satellite images [C] . 2025 : 226-229 . |
| MLA | Cai, Xiaojiao et al. "Building types classification using MHGNN from high-resolution satellite images" . (2025) : 226-229 . |
| APA | Cai, Xiaojiao , Li, Mengmeng , Zha, Ying . Building types classification using MHGNN from high-resolution satellite images . (2025) : 226-229 . |
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Building extraction from remote sensing imagery is vital for various human activities. But it is challenging due to diverse building appearances and complex backgrounds. Research shows the importance of both global context and spatial details for accurate building extraction. Therefore, methods integrating convolutional neural networks (CNNs) and visual transformers (ViTs) are popular nowadays. However, current methods combining these two methods inadequately merge their features and only perform decoding once, leading to issues like unclear boundaries, internal voids, and susceptibility to non-building elements in complex scenarios with low inter-class and high intra-class variability. To address these issues, this paper introduces a novel extraction method called TDFNet. We first replace ViT with V-Mamba, which has linear complexity, and combine it with CNN for feature extraction. A bidirectional fusion module (BFM) is then designed to comprehensively integrate spatial details and global information, thereby enabling accurate identification of boundaries between adjacent buildings, and maintaining the structural integrity of buildings to avoid internal holes. During the decoding process, we propose an Encoder-Decoder Fusion Module (EDFM) to initially merge features from different stages of the encoder and decoder, thereby diminishing the model's susceptibility to non-building elements with features similar to those of buildings, and consequently reducing the incidence of erroneous extractions. Subsequently, a twice decoding strategy is implemented to enhance the learning of multi-scale features significantly, thereby mitigating the impact of tree occlusions and shadows. Our method yields the state-of-the-art (SOTA) performance on three public building datasets.
Keyword ：
Building extraction Building extraction remote sensing remote sensing twice decoding twice decoding V-Mamba V-Mamba
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| GB/T 7714 | Wang, Wenlong , Yu, Peng , Li, Mengmeng et al. TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction [J]. | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
| MLA | Wang, Wenlong et al. "TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction" . | GEO-SPATIAL INFORMATION SCIENCE (2025) . |
| APA | Wang, Wenlong , Yu, Peng , Li, Mengmeng , Zhong, Xiaojing , He, Yuanrong , Su, Hua et al. TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction . | GEO-SPATIAL INFORMATION SCIENCE , 2025 . |
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Accurate information on land use classification is essential for promoting sustainable urban growth, preserving the environment, safeguarding public health, and enhancing socioeconomic prosperity. This paper explores the application of street view image (SVI) assisted remote sensing images (RSI) for urban land use classification. In this paper, we introduced a dual-stream network integrating a large model and CNN for feature extraction, termed the Large-VGG Dual-Stream Network (LVDNet), which extract RSI semantic features using a CNN backbone, while SVI semantic sementic features are obtained from a large model trained on real-world data, with feature fusion achieved through cross-learning. We constructed a new dataset ZZ for experiments, in which the spatial correspond between street view images and remote sensing imagery is established. Our findings indicate that the proposed method performs effectively in datasets, demonstrates SVI significantly enhance the accuracy of urban land use classification, particularly in identifying complex urban functional areas. © 2025 IEEE.
Keyword ：
Classification (of information) Classification (of information) Image classification Image classification Land use Land use Public health Public health Remote sensing Remote sensing Semantics Semantics Semantic Web Semantic Web Urban growth Urban growth
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| GB/T 7714 | Yu, Zheyao , Li, Mengmeng , Cai, Xiaojiao . Urban Land Use Classification with Street View Image-Assisted Remote Sensing Images [C] . 2025 : 230-233 . |
| MLA | Yu, Zheyao et al. "Urban Land Use Classification with Street View Image-Assisted Remote Sensing Images" . (2025) : 230-233 . |
| APA | Yu, Zheyao , Li, Mengmeng , Cai, Xiaojiao . Urban Land Use Classification with Street View Image-Assisted Remote Sensing Images . (2025) : 230-233 . |
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Semantic change detection (SCD) in high-resolution (HR) remote sensing images faces two issues: (1) isolated network branch for binary change detection (BCD) within multi-task architecture result in suboptimal SCD performance; (2) false alarms or missed detections caused by illumination differences or seasonal transform. To address these issues, this study proposes a bi-temporal binary change enhancement network (Bi-BCENet). Specifically, we introduce a binary change enhancement (BCE) strategy based on multi-network joint learning to achieve superior SCD via improving change areas prediction. Within the network’s reasoning process, we develop a cross-attention fusion module (CAFM) to enhance the global similarity modeling via cross-network prompt fusion, and we employ a cosine similarity-based auxiliary loss to optimize non-change’s semantic consistency. The experiments on SECOND and CINA-FX datasets demonstrate that Bi-BCENet outperforms representative SCD networks, achieving 62.08%, 84.95% in FSCD and 66.88%, 83.10% in mIoUsCD, respectively. And the ablation analysis of network validates Bi-BCENet’s effectiveness in reducing false alarms and missed detections in SCD results. Moreover, for specific SCD of cropland, Bi-BCENet shows its strong potential in single-to-multi SCD. © 2004-2012 IEEE.
Keyword ：
binary change detection binary change detection High-resolution remote sensing High-resolution remote sensing joint learning joint learning semantic change detection semantic change detection
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| GB/T 7714 | Lin, H. , Wang, X. , Wu, Q. et al. Semantic Change Detection in HR Remote Sensing Images with Joint Learning and Binary Change Enhancement Strategy [J]. | IEEE Geoscience and Remote Sensing Letters , 2025 . |
| MLA | Lin, H. et al. "Semantic Change Detection in HR Remote Sensing Images with Joint Learning and Binary Change Enhancement Strategy" . | IEEE Geoscience and Remote Sensing Letters (2025) . |
| APA | Lin, H. , Wang, X. , Wu, Q. , Li, M. , Yang, Z. , Lou, K. . Semantic Change Detection in HR Remote Sensing Images with Joint Learning and Binary Change Enhancement Strategy . | IEEE Geoscience and Remote Sensing Letters , 2025 . |
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Change detection in remote sensing images is crucial for assessing human activity impacts and supporting government decision-making. However, in practice, obtaining bitemporal remote sensing images with consistent conditions is highly limited, and existing change detection methods still face two main challenges: 1) in real-world scenarios, inconsistent sensor and lighting conditions cause significant style (visual appearance) differences between bitemporal remote sensing images, leading to false changes and reducing change detection accuracy and 2) remote sensing images contain complex semantic information, and complex scenarios such as shadow occlusion and seasonal vegetation changes make the existing methods difficult to capture relevant features related to change areas. To address these challenges, we propose a style consistency enhanced differential network (SCEDNet) to eliminate style discrepancies between temporally distinct images and enhance the semantic information of change features. Specifically, we introduce a style consistency module (SCM) in the encoder to extract consistent features by computing the mean and variance of temporal features. Then, we introduce an enhanced differential module (EDM) to enhance change semantics, tackling issues such as mislocalization and incomplete regions in complex cases such as shadow occlusion and seasonal vegetation changes. In addition, we design a gate fusion upsampling (GFU) and change refine module (CRM) in the decoder to integrate multilevel differential features with different semantic information and highlight key changes, further improving change detection performance. Experiments on the CDD and GZ_CD datasets show that SCEDNet outperformed eight methods, achieving F1-scores of 95.59% and 90.41%, respectively. Code and datasets are available at https://github.com/Yzwfff/SCEDNet
Keyword ：
Change detection Change detection deep learning deep learning enhanced differential features enhanced differential features remote sensing images remote sensing images style consistency style consistency
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| GB/T 7714 | Yang, Zhiwei , Wang, Xiaoqin , Li, Mengmeng et al. SCEDNet: A Style Consistency Enhanced Differential Network for Remote Sensing Image Change Detection [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
| MLA | Yang, Zhiwei et al. "SCEDNet: A Style Consistency Enhanced Differential Network for Remote Sensing Image Change Detection" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 22 (2025) . |
| APA | Yang, Zhiwei , Wang, Xiaoqin , Li, Mengmeng , Long, Jiang . SCEDNet: A Style Consistency Enhanced Differential Network for Remote Sensing Image Change Detection . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
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Precise information on agricultural parcels is crucial for effective farm management, crop mapping, and monitoring. Current techniques often encounter difficulties in automatically delineating vectorized parcels from remote sensing images, especially in irregular-shaped areas, making it challenging to derive closed and vectorized boundaries. To address this, we treat parcel delineation as identifying valid parcel vertices from remote sensing images to generate parcel polygons. We introduce a Point-Line-Region interactive multitask network (PLR-Net) that jointly learns semantic features of parcel vertices, boundaries, and regions through point-, line-, and region-related subtasks within a multitask learning framework. We derived an attraction field map (AFM) to enhance the feature representation of parcel boundaries and improve the detection of parcel regions while maintaining high geometric accuracy. The point-related subtask focuses on learning features of parcel vertices to obtain preliminary vertices, which are then refined based on detected boundary pixels to derive valid parcel vertices for polygon generation. We designed a spatial and channel excitation module for feature interaction to enhance interactions between points, lines, and regions. Finally, the generated parcel polygons are refined using the Douglas-Peucker algorithm to regularize polygon shapes. We evaluated PLR-Net using high-resolution GF-2 satellite images from the Shandong, Xinjiang, and Sichuan provinces of China and medium-resolution Sentinel-2 images from The Netherlands. Results showed that our method outperformed existing state-of-the-art techniques (e.g., BsiNet, SEANet, and Hisup) in pixel- and object-based geometric accuracy across all datasets, achieving the highest IoU and polygonal average precision on GF2 datasets (e.g., 90.84% and 82.00% in Xinjiang) and on the Sentinel-2 dataset (75.86% and 47.1%). Moreover, when trained on the Xinjiang dataset, the model successfully transferred to the Shandong dataset, achieving an IoU score of 83.98%. These results demonstrate that PLR-Net is an accurate, robust, and transferable method suitable for extracting vectorized parcels from diverse regions and types of remote sensing images. The source codes of our model are available at https://github.com/mengmengli01/PLR-Net-demo/tree/main.
Keyword ：
Agricultural parcel delineation Agricultural parcel delineation Multitask neural networks Multitask neural networks PLR-Net PLR-Net Point-line-region interactive Point-line-region interactive Vectorized parcels Vectorized parcels
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| GB/T 7714 | Li, Mengmeng , Lu, Chengwen , Lin, Mengjing et al. Extracting vectorized agricultural parcels from high-resolution satellite images using a Point-Line-Region interactive multitask model [J]. | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 231 . |
| MLA | Li, Mengmeng et al. "Extracting vectorized agricultural parcels from high-resolution satellite images using a Point-Line-Region interactive multitask model" . | COMPUTERS AND ELECTRONICS IN AGRICULTURE 231 (2025) . |
| APA | Li, Mengmeng , Lu, Chengwen , Lin, Mengjing , Xiu, Xiaolong , Long, Jiang , Wang, Xiaoqin . Extracting vectorized agricultural parcels from high-resolution satellite images using a Point-Line-Region interactive multitask model . | COMPUTERS AND ELECTRONICS IN AGRICULTURE , 2025 , 231 . |
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Building type information indicates the functional properties of buildings and plays a crucial role in smart city development and urban socioeconomic activities. Existing methods for classifying building types often face challenges in accurately distinguishing buildings between types while maintaining well-delineated boundaries, especially in complex urban environments. This study introduces a novel framework, i.e., CNN-Transformer cross-attention feature fusion network (CTCFNet), for building type classification from very high resolution remote sensing images. CTCFNet integrates convolutional neural networks (CNNs) and Transformers using an interactive cross-encoder fusion module that enhances semantic feature learning and improves classification accuracy in complex scenarios. We develop an adaptive collaboration optimization module that applies human visual attention mechanisms to enhance the feature representation of building types and boundaries simultaneously. To address the scarcity of datasets in building type classification, we create two new datasets, i.e., the urban building type (UBT) dataset and the town building type (TBT) dataset, for model evaluation. Extensive experiments on these datasets demonstrate that CTCFNet outperforms popular CNNs, Transformers, and dual-encoder methods in identifying building types across various regions, achieving the highest mean intersection over union of 78.20% and 77.11%, F1 scores of 86.83% and 88.22%, and overall accuracy of 95.07% and 95.73% on the UBT and TBT datasets, respectively. We conclude that CTCFNet effectively addresses the challenges of high interclass similarity and intraclass inconsistency in complex scenes, yielding results with well-delineated building boundaries and accurate building types.
Keyword ：
Accuracy Accuracy Architecture Architecture Buildings Buildings Building type classification Building type classification CNN-transformer networks CNN-transformer networks cross-encoder cross-encoder Earth Earth Feature extraction Feature extraction feature interaction feature interaction Optimization Optimization Remote sensing Remote sensing Semantics Semantics Transformers Transformers very high resolution remote sensing very high resolution remote sensing Visualization Visualization
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| GB/T 7714 | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan et al. Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 976-994 . |
| MLA | Zhang, Shaofeng et al. "Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 976-994 . |
| APA | Zhang, Shaofeng , Li, Mengmeng , Zhao, Wufan , Wang, Xiaoqin , Wu, Qunyong . Building Type Classification Using CNN-Transformer Cross-Encoder Adaptive Learning From Very High Resolution Satellite Images . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 976-994 . |
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Semantic change detection (SCD) aims to identify potential Earth surface changes, including their location and class, from multitemporal remote sensing images. However, the underdetection and pseudochange issues in existing SCD methods severally limit their effectiveness in diverse ground scenarios. To address these issues, a semantic map-guided network, namely, SMGNet, is proposed based on a multitask architecture designed to identify potential land-cover changes from bitemporal high-resolution remote sensing images. A robust feature extractor is first developed to extract multiscale contextual information while retaining fine-grained spatial details, thus enhancing the semantic representation of complex objects with irregular shapes and large sizes. To address the issue of underdetection, we integrate historical semantic information derived from pretemporal land-cover maps into the model using a semantic map encoder module. A semantic fusion module based on Bayesian theory is developed to highlight salient changed information, thus reducing pseudochanges caused by the same ground objects with spectra variations. Experimental results obtained in a public SCD dataset demonstrate the effectiveness of the proposed method in identifying various semantic changes. Results indicate that the proposed SMGNet achieved the highest detection accuracy, exceeding nine existing methods by 14.81%-41.28% and 8.45%-40.31% in terms of separated kappa (SeK) and F1 -score ( F1(scd) ) metrics on the high-resolution SCD (HRSCD) dataset, respectively. The proposed method effectively alleviated pseudochanges induced by spectra and temporal differences, and accurately detecting these changed objects with irregular shapes and large sizes. The detected results exhibited high interclass compactness and well-defined boundaries. Code and data are available at https://github.com/long123524/SMGNet
Keyword ：
Bayes methods Bayes methods Data mining Data mining Feature extraction Feature extraction Historical semantic information Historical semantic information Land surface Land surface Multitasking Multitasking pseudochanges pseudochanges Remote sensing Remote sensing remote sensing semantic change detection (SCD) remote sensing semantic change detection (SCD) semantic map-guided network (SMGNet) semantic map-guided network (SMGNet) Semantics Semantics Shape Shape Training Training Transformers Transformers underdetection underdetection
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| GB/T 7714 | Long, Jiang , Liu, Sicong , Li, Mengmeng . SMGNet: A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
| MLA | Long, Jiang et al. "SMGNet: A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 22 (2025) . |
| APA | Long, Jiang , Liu, Sicong , Li, Mengmeng . SMGNet: A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2025 , 22 . |
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