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学者姓名：汪小钦
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Abstract ：
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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Normalization of satellite images collected under various atmospheric conditions is critical for the comprehensive, long-term global surveillance of terrestrial surface alterations. This study utilized remote sensing data from the Sentinel-2A Multispectral Instrument (MSI) in polar orbit and the Landsat-8 Operational Land Imager (OLI) sensors, with multispectral global coverage of 10-30 m, to derive reflectance products using inversion algorithms. Validation and assessment were conducted using synchronous surface measurement spectra collected from four sites across three Chinese provinces in 2019. We corrected surface reflectance and derived vegetation indices across blue, green, red, near-infrared (NIR), and two short-wave infrared (SWIR) bands and normalized discrepancies. The phenological spatial distribution map for late rice in Jiangxi Province was constructed using normalized data outcomes. A robust linear correlation in reflectance across corresponding bands of the two satellite sensors was observed. The NIR and SWIR bands showed the most significant difference because of differences in their spectral response functions. A high degree of congruence was observed between Landsat-8 OLI and Sentinel-2 MSI sensor reflectance products, with root mean square error values consistently below 0.05. The derived conversion equations were highly accurate for harmonizing data from both sensor systems.
Keyword ：
harmonization harmonization Landsat-8 OLI Landsat-8 OLI Sentinel-2 MSI Sentinel-2 MSI Surface reflectance (SR) Surface reflectance (SR) vegetation index vegetation index
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| GB/T 7714 | Zhang, Jiaqi , Zhou, Xiaocheng , Liu, Xueping et al. Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Zhang, Jiaqi et al. "Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Zhang, Jiaqi , Zhou, Xiaocheng , Liu, Xueping , Wang, Xiaoqin , He, Guojin , Zhang, Youshui . Harmonizing Landsat-8 OLI and Sentinel-2 MSI: an assessment of surface reflectance and vegetation index consistency . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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针对三维绿量计算过程中树木参数获取成本较高的问题,提出一种融合高分辨率遥感数据和街景数据的城市行道树三维绿量估算方法.以福州主要辖区为例,首先基于高分二号(GF-2)遥感影像,获取城市行道树的二维分布;然后结合街景地图实现对行道树的树木参数量测;最后基于行道树的水平分布和垂直特征完成三维绿量的估算.结果表明,研究区内行道树整体分布不均衡.基于街景测量获取的树木参数精度较高,与实测数据相比,R2 大于 0.9.单位面积上的三维绿量在白马路路段较高,在福马路等路段较低,榕树对该研究区的三维绿量贡献最大,占研究区总绿量的 80%.与二维指标相比,城市行道树的三维绿量值更能体现城市行道树的三维立体差异,反映绿地实际生态效益.
Keyword ：
三维绿量 三维绿量 百度街景 百度街景 立体景观 立体景观 虚拟测量 虚拟测量 行道树 行道树
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| GB/T 7714 | 孔令凤 , 汪小钦 , 周小成 . 融合遥感影像与街景的城市行道树三维绿量估算 [J]. | 福州大学学报（自然科学版） , 2025 , 53 (2) : 151-158 . |
| MLA | 孔令凤 et al. "融合遥感影像与街景的城市行道树三维绿量估算" . | 福州大学学报（自然科学版） 53 . 2 (2025) : 151-158 . |
| APA | 孔令凤 , 汪小钦 , 周小成 . 融合遥感影像与街景的城市行道树三维绿量估算 . | 福州大学学报（自然科学版） , 2025 , 53 (2) , 151-158 . |
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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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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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The evaluation of regional ecological status has far-reaching significance for understanding regional ecological conditions and promoting sustainable development. Herein, a geospatial ecological index (GEI) was developed on the basis of Landsat data and the principles of soil lines and spatial geometry. Specifically, the GEI integrates four remote sensing indicators: Perpendicular Vegetation Index (PVI) representing greenness, Modified Perpendicular Drought Index (MPDI) representing drought, Normalized Difference Built-up and Soil Index (NDSI) representing the dryness of land surface, and Land Surface Temperature (LST) representing the hotness of land surface. Two typical regions, Fuzhou City and Zijin mining area, in Fujian Province, China, were selected to evaluate regional ecological quality via the proposed GEI. The results show an improvement in the overall ecological quality of Fuzhou City, with an increase in the average GEI value from 0.49 in 2001 to 0.53 in 2020. In the case of the Zijin mining area, regions with poor ecological status are concentrated in the main mining areas. However, the average GEI value rose from 0.51 in 1992 to 0.57 in 2020, illustrating an improvement in its ecological conditions. The study demonstrates the robustness and effectiveness of GEI, objectively revealing the spatial distribution and ecological status.
Keyword ：
ecological evaluation ecological evaluation fujian fujian GEI (Geospatial Ecological Index) GEI (Geospatial Ecological Index) geometric space geometric space soil line soil line
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| GB/T 7714 | Lin, Mengjing , Zhao, Yang , Shi, Longyu et al. A novel ecological evaluation index based on geospatial principles and remote sensing techniques [J]. | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 , 31 (7) : 809-826 . |
| MLA | Lin, Mengjing et al. "A novel ecological evaluation index based on geospatial principles and remote sensing techniques" . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY 31 . 7 (2024) : 809-826 . |
| APA | Lin, Mengjing , Zhao, Yang , Shi, Longyu , Wang, Xiaoqin . A novel ecological evaluation index based on geospatial principles and remote sensing techniques . | INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY , 2024 , 31 (7) , 809-826 . |
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A healthy ecological environment forms a crucial foundation for the sustainable development of both nations and humanity. In the domain of ecological environment assessment, the comprehensive indicator system model represents the mainstream evaluation approach, both domestically and internationally. The extensive application of big geodata within this context offers significant potential for addressing ecological problems characterized by vast scales, intricate processes, and a variety of influencing factors. However, as the acquisition of big geodata becomes increasingly accessible, the coverage of the index system has significantly expanded, raising the pivotal issue of objectively and scientifically selecting crucial indicators capable of representing the distinctive characteristics of the study area. This challenge is particularly critical in today's ecological health assessment. The Pressure-State-Response (PSR) model offers a causal perspective, comprehensively considering the systemic relationships between the ecological environment and human socioeconomic activities. The Ecological Hierarchy Network (EHN) model is capable of reflecting the overlap and interconnections between upper and lower-layer indicators. In this study, by integrating the frameworks of PSR and EHN and taking into account the potential information overlap from multiple available parameters, we established a five-layer networked indicator system consisting of the Target Layer, Criteria Layer, Element Layer, Indicator Layer, and Homogeneous Indicator Layer. We also proposed a two-stage adaptive indicator reduction model that combines Homogeneous Indicator Layer reduction using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Indicator Layer reduction based on target optimization theory. Combining both approaches, we developed an adaptive indicator reduction model tailored for ecological environmental health assessment. Leveraging big geodata comprising remote sensing thematic products, topography, meteorology, soil, and population information, we applied the proposed model to assess the ecological health of seven ecologically diverse regions in China, including Yunnan, Fujian, Beijing-Tianjin-Hebei, Shaanxi, Hubei, Xinjiang, and Jilin during the period 2001-2021. The results show that: (1) The selected indicators obtained through the two-stage indicator adaptive reduction model effectively reflected the distinct characteristics of ecosystems in different regions. Furthermore, indicators with higher weights among the selected ones have been widely employed in constructing indicator systems across various regions. These findings highlighted the universality and rationality of both the constructed indicator system and the two-stage indicator adaptive reduction model, effectively mitigating the subjectivity associated with manual indicator system construction; (2) The spatial distribution and temporal trends of the ecological environment health of the seven regions aligned with real-world conditions and were corroborated by existing literature and data, which indicated the effectiveness of the model proposed in this study. The proposed models presented in this paper can serve as a reference for constructing indicator systems and selecting indicators in other domains and provide methodological support for ecological environment health assessment across diverse regions on a large scale. © 2024 Science Press. All rights reserved.
Keyword ：
Ecosystems Ecosystems Geographic information systems Geographic information systems Optimization Optimization Remote sensing Remote sensing Site selection Site selection Sustainable development Sustainable development Topography Topography
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| GB/T 7714 | Chen, Jianhui , Wang, Xiaoqin , Kong, Lingfeng . Construction of Adaptive Indicator Reduction Model for Ecological Environment Health Assessment [J]. | Journal of Geo-Information Science , 2024 , 26 (5) : 1193-1211 . |
| MLA | Chen, Jianhui et al. "Construction of Adaptive Indicator Reduction Model for Ecological Environment Health Assessment" . | Journal of Geo-Information Science 26 . 5 (2024) : 1193-1211 . |
| APA | Chen, Jianhui , Wang, Xiaoqin , Kong, Lingfeng . Construction of Adaptive Indicator Reduction Model for Ecological Environment Health Assessment . | Journal of Geo-Information Science , 2024 , 26 (5) , 1193-1211 . |
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The construction of China's ecological civilization, known as 'Beautiful China', necessitates implementing precision watershed management through scientifically informed decision-making. This entails optimizing the spatial distribution of watershed best management practices (the so- called BMP scenario) and proposing multistage implementation plans, or roadmaps that align with practical requirements based on the overarching vision of comprehensive water shed management.The'water shed system simulation-scenariooptimization' method frame work (the simulation-and-optimization-based frame work for short) has demonstrated considerable potential in recent years. To address challenges arising from practical applications of this framework, this study systematically conducted the methodological research: (1) proposing a novel watershed process modeling framework that strikes a balance between modeling flexibility and high-performance computing to model and simulate watershed systems efficiently; (2) introducing slope position units as BMP configuration units and enabling dynamic boundary adjustments during scenario optimization, effectively incorporating practical knowledge of watershed management to ensure reasonable outcomes; (3) presenting an optimization method for determining the implementation orders of BMPs that considers stepwise investment constraints, thereby recommending feasible roadmaps that meet practical needs; and (4) designing a user-friendly participatory watershed planning system to facilitate collaborative decision-making among stakeholders. The effectiveness and practical value of these new methods, tools, and prototype systems are validated through application cases in a representative small watershed. This research contributes to advancing precision watershed management and provides valuable insights for sustainable ecological conservation. The methods proposed within the simulation-and-optimization-based framework in this study are universal methods, which means their application does not depend on the specific implementation, such as the watershed process model, the BMP types considered, the designed BMP configuration strategy, and so on. Further studies should be conducted not only to deepen related theory and method research but also to strengthen promotion and application, especially cooperating with local watershed management agents to provide valuable insights for their sustainable ecological conservation. © 2024 Science Press. All rights reserved.
Keyword ：
Computation theory Computation theory Decision making Decision making Decision support systems Decision support systems Ecology Ecology Investments Investments Soil conservation Soil conservation Water conservation Water conservation Water management Water management Watersheds Watersheds
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| GB/T 7714 | Qin, Chengzhi , Zhu, Liangjun , Shen, Shen et al. Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization [J]. | Acta Geographica Sinica , 2024 , 79 (1) : 58-75 . |
| MLA | Qin, Chengzhi et al. "Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization" . | Acta Geographica Sinica 79 . 1 (2024) : 58-75 . |
| APA | Qin, Chengzhi , Zhu, Liangjun , Shen, Shen , Wu, Tong , Xiao, Guirong , Wu, Sheng et al. Methods for supporting decision-making of precision watershed management based on watershed system simulation and scenario optimization . | Acta Geographica Sinica , 2024 , 79 (1) , 58-75 . |
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Soil erosion constitutes a critical environmental issue with far-reaching ramifications. Vegetation cover has been identified as a key factor in mitigating soil erosion. This study utilized remote sensing data and cloud computing resources provided by Google Earth Engine (GEE) to compute the land cover classification and Fractional Vegetation Cover (FVC) of the Minjiang River Basin in 2020. Subsequent to the utilization of the CSLE model incorporating precipitation data, Digital Elevation Model (DEM) data, and other relevant data, an assessment of soil erosion in the Minjiang River basin during 2020 was conducted. Furthermore, the correlation between FVC and soil erosion modulus was quantitatively examined. Our findings demonstrate that the predominant land cover type in the Minjiang River basin is forest and grassland, followed by arable land, with water having the smallest coverage. Over 65%of the study are a exhibits a FVC exceeding 0.8, indicative of a generally high level of vegetation coverage. The soil erosion modulus exhibited a marked decline with increasing FVC within the FVC intervals of 0-0.15 and 0.45-0.8. While the change of soil erosion modulus with FVC under other ranges was relatively flat. As such, erosion control measures may be more effective when implemented in the FVC ranges of 0-0.15 and 0.45-0.80. These findings provide decision-making references for governmental departments to formulate targeted soil and water conservation measures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
Keyword ：
Decision making Decision making Erosion Erosion Forestry Forestry Remote sensing Remote sensing Rivers Rivers Soil conservation Soil conservation Soils Soils Surveying Surveying Vegetation Vegetation Water conservation Water conservation Watersheds Watersheds
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| GB/T 7714 | Chen, Miao , Wang, Xiaoqin . Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology [C] . 2024 . |
| MLA | Chen, Miao et al. "Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology" . (2024) . |
| APA | Chen, Miao , Wang, Xiaoqin . Analyzing the relationship between vegetation cover and soil erosion in the Minjiang river basin using remote sensing technology . (2024) . |
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Visual foundation models (VFMs) pretrained on large-scale training datasets show robust zero-shot adaptability across many vision tasks. However, there still exist limitations in remote sensing processing tasks due to the variety and complexity of remote sensing images. In this letter, we propose a two-flow network (TFNet) based on multitask VFM, named TFNet, to extract croplands with well-delineated boundaries from high-resolution remote sensing images. TFNet consists of a mask flow and a boundary flow. It first uses a VFM as visual encoder to obtain universal semantic features regarding croplands and then aggregates them into the two flows. Next, a boundary prior-guided module (BPM) is developed to incorporate boundary semantics derived from the boundary flow into the mask flow, to refine the boundary details of croplands. We also develop a multibranch parallel fusion module (MPFM) that aggregates multiscale contextual information to improve the identification of cropland with varied sizes and shapes. Finally, a semantic consistency loss is introduced to further optimize the feature learning of cropland information. We conducted extensive experiments on Shandong (SD) and Xinjiang (XJ) datasets collected from Gaofen-2 (GF-2) satellites and compared our method with five existing methods. Experimental results show that the croplands extracted by our method have the fewest omissions and errors, achieving the highest attribute accuracy (intersection over union (IoU) of 0.863 and 0.945) and lowest geometric errors (global total classification (GTC) of 0.134 and 0.097) than other methods on the two datasets. Our method effectively distinguished croplands of varied sizes, shapes, and spectra, even in scenarios with limited samples. Code and datasets are available at https://github.com/long123524/TFNet.
Keyword ：
Boundary prior Boundary prior cropland extraction cropland extraction high-resolution remote sensing images high-resolution remote sensing images limited samples limited samples two-flow network (TFNet) two-flow network (TFNet) visual foundation model (VFM) visual foundation model (VFM)
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| GB/T 7714 | Long, Jiang , Zhao, Hang , Li, Mengmeng et al. Integrating Segment Anything Model Derived Boundary Prior and High-Level Semantics for Cropland Extraction From High-Resolution Remote Sensing Images [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
| MLA | Long, Jiang et al. "Integrating Segment Anything Model Derived Boundary Prior and High-Level Semantics for Cropland Extraction From High-Resolution Remote Sensing Images" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21 (2024) . |
| APA | Long, Jiang , Zhao, Hang , Li, Mengmeng , Wang, Xiaoqin , Lu, Chengwen . Integrating Segment Anything Model Derived Boundary Prior and High-Level Semantics for Cropland Extraction From High-Resolution Remote Sensing Images . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
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