International Journal of Applied Earth Observation and Geoinformation最新文献

PyramidMamba: Rethinking pyramid feature fusion with selective space state model for semantic segmentation of remote sensing imagery 金字塔曼巴：基于选择性空间状态模型的金字塔特征融合遥感图像语义分割

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2025-10-04 DOI: 10.1016/j.jag.2025.104884

Libo Wang, Dongxu Li, Sijun Dong, Xiaoliang Meng, Xiaokang Zhang, Danfeng Hong

{"title":"PyramidMamba: Rethinking pyramid feature fusion with selective space state model for semantic segmentation of remote sensing imagery","authors":"Libo Wang, Dongxu Li, Sijun Dong, Xiaoliang Meng, Xiaokang Zhang, Danfeng Hong","doi":"10.1016/j.jag.2025.104884","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104884","url":null,"abstract":"Semantic segmentation, as a basic tool for remote sensing image understanding, plays a vital role in many Earth Observation (EO) applications. Nowadays, accurate semantic segmentation of remote sensing images remains a challenge due to the complex spatial–temporal scenes and multi-scale geo-objects. Driven by the wave of deep learning (DL), CNN– and Transformer-based semantic segmentation methods have been explored widely, and these two architectures both revealed the importance of multi-scale feature representation for strengthening semantic information of geo-objects. However, multi-scale feature fusion often comes with the semantic redundancy issue due to homogeneous semantic contents in pyramid features. To handle this issue, we propose a novel Mamba-based segmentation network, namely PyramidMamba. Specifically, we design a plug-and-play Mamba-based decoder, which develops a dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in feature fusion. Ablation experiments illustrate the effectiveness and superiority of the proposed method in enhancing multi-scale feature representation as well as the great potential for real-time semantic segmentation. Moreover, our PyramidMamba yields state-of-the-art performance on four public datasets, i.e. the OpenEarthMap (70.8% mIoU), ISPRS Vaihingen (84.8% mIoU) and Potsdam (88.0% mIoU) datasets, and the LoveDA (54.8% mIoU) dataset.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets 基于双分支时空转换器的小数据集冬小麦提取跨区域可转移性研究

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2025-08-10 DOI: 10.1016/j.jag.2025.104785

Chenyang He, Jia Song

{"title":"A dual-branch spatio-temporal Transformer for enhancing cross-regional transferability of winter wheat extraction using small training datasets","authors":"Chenyang He, Jia Song","doi":"10.1016/j.jag.2025.104785","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104785","url":null,"abstract":"Accurate identification of winter wheat from remote sensing imagery is crucial for large-scale agricultural monitoring. Despite the success of Transformer-based deep learning models in various fields, their application in crop identification has been limited by the scarcity of extensive labeled training data. This study proposes a dual-branch spatio-temporal Transformer (DST-Transformer) for winter wheat extraction from Sentinel-2 imagery using a small training dataset. By independently extracting temporal and spatial features, the DST-Transformer effectively delineates crop boundaries and reduces misclassification. Experiments demonstrate its effectiveness with small training datasets, achieving over 90% overall accuracy (OA) and 88.25% mean intersection over union (MIoU) when evaluating on test datasets. The DST-Transformer was further applied to large-scale winter wheat extraction across Shandong Province, China (an area 66 times larger than the training region) to evaluate its cross-regional transferability. Evaluation results showed OA over 92% and MIoU exceeding 85% at all validation sites, highlighting the DST-Transformer’s robustness and strong generalization capability. This study underscores the DST-Transformer’s potential for large-scale crop identification and illustrates the promise of Transformer-based architectures for efficient, high-precision crop mapping with small training datasets, advancing the application of deep learning in agricultural remote sensing.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"7 1","pages":"104785"},"PeriodicalIF":7.5,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic seismic source modeling of InSAR displacements InSAR位移的自动震源建模

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103445

S. Atzori, Fernando Monterroso, A. Antonioli, C. Luca, N. Svigkas, F. Casu, M. Manunta, M. Quintiliani, R. Lanari

引用次数: 0

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States. 基于深度学习的美国精细分辨率建筑用地数据的空间显式精度评估。

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 Epub Date: 2023-08-28 DOI: 10.1016/j.jag.2023.103469

Johannes H Uhl, Stefan Leyk

{"title":"Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.","authors":"Johannes H Uhl, Stefan Leyk","doi":"10.1016/j.jag.2023.103469","DOIUrl":"10.1016/j.jag.2023.103469","url":null,"abstract":"<p><p>Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.</p>","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"123 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54752685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Optimal spectral index and threshold applied to Sentinel-2 data for extracting impervious surface: Verification across latitudes, growing seasons, approaches, and comparison to global datasets 应用于Sentinel-2数据提取不透水地表的最佳光谱指数和阈值:跨纬度、生长季节、方法的验证，以及与全球数据集的比较

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103470

Y. Dvornikov, V. Grigorieva, M. Varentsov, V. Vasenev

引用次数: 0

The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions 无人机(UAV)测量和GIS在游憩步道条件分析与监测中的应用

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103474

A. Tomczyk, M. Ewertowski, Noah Creany, F. Ancin‐Murguzur, Christopher Monz

引用次数: 1

A polarization-spectrum fusion framework based on multiscale transform and generative adversarial network for improving water and different vegetation distinguishability 基于多尺度变换和生成对抗网络的偏振光谱融合框架提高水体和不同植被的可分辨性

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103468

Qihao Chen, Mengqing Pang, Xiuguo Liu, Zeyu Zhang

引用次数: 0

HCPNet: Learning discriminative prototypes for few-shot remote sensing image scene classification HCPNet:基于少拍遥感影像场景分类的判别原型学习

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103447

Junjie Zhu, Ke Yang, Naiyang Guan, Xiaodong Yi, C. Qiu

引用次数: 1

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103467

Shen Ying, Xinyue Zhang, Meng Wang, Han Guo

引用次数: 0

On the ability of dual-polarimetric SAR measurements to observe lava flows under different volcanic environments 双极化SAR观测不同火山环境下熔岩流的能力研究

IF 7.5 1区地球科学

International Journal of Applied Earth Observation and Geoinformation Pub Date : 2023-09-01 DOI: 10.1016/j.jag.2023.103471

E. Ferrentino, C. Bignami, F. Nunziata, S. Stramondo, M. Migliaccio

引用次数: 0