Cited by A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood

CPVF: vectorization of agricultural cultivation field parcels via a boundary–parcel multi-task learning network in ultra-high-resolution remote sensing images DOI

Xiuyu Liu,

Jinshui Zhang,

Yaming Duan

и другие.

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2025, Номер 226, С. 267 - 299

Опубликована: Май 26, 2025

Язык: Английский

Процитировано

Unsupervised Deep Learning for Flood Segmentation in UAV Imagery DOI

Georgios Simantiris, Costas Panagiotakis

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 408 - 423

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

Spatiotemporal Monitoring of Saline Water Body Changes Using Remote Sensing Data with a Focus on Comparing Spectral Indices (Case Study: Lake Urmia) DOI

Ali Rezaali,

Hamid Ebadi, Hadi Farhadi

и другие.

Deleted Journal, Год журнала: 2025, Номер 14(3), С. 69 - 87

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

Improvement and application of UNet network for avoiding the effect of urban dense high-rise buildings and other feature shadows on water body extraction DOI

Yiheng Xie, Renxi Chen,

Mingge Yu

и другие.

International Journal of Remote Sensing, Год журнала: 2023, Номер 44(12), С. 3861 - 3891

Опубликована: Июнь 18, 2023

ABSTRACTFinding a means to extract water body information efficiently and accurately from high-resolution remote sensing images has been an important research direction in the field of extraction recent years. However, shadows buildings other obstacles interfere with accuracy extraction. To address this problem, paper proposes neural network method incorporating attention mechanism for This is based on U-Net convolutional adds squeeze-and-excitation module SENet, mechanism, downsampling process network. The weights feature maps so that focuses more features thus reduces shadow features, improving image segmentation. dropout batch normalization layers are also added improve generalization ability stability model. In paper, SE-CU-Net model presented overcome shadowing effect features. Using GF-2 Jiangsu province as data source, recognition results compared Dense-Net, Res-Net, Seg-Net, U-net, SVM, RF. Through comparison experiments, can not only better influence but it stronger effect. average ASCR, Precision, mIoU, OA, F1-Score kappa coefficients three tested areas reached 98.27%, 97.17%, 89.33%, 98.2%, 89.3% 0.883, respectively, significantly higher than six classical methods, verifying effectiveness overcoming research.KEYWORDS: extractiondeep learningshadows buildingsU-Netattention Disclosure statementNo potential conflict interest was reported by author(s).Additional informationFundingThis work Funded Key Laboratory Land Satellite Remote Sensing Application, Ministry Natural Resources People's Republic China(Grant No. KLSMNR-G202212)

Язык: Английский

Процитировано

A novel convolutional neural network model with hybrid attentional atrous convolution module for detecting the areas affected by the flood DOI

Abdullah ŞENER, Gürkan Doğan, Burhan Ergen

и другие.

Earth Science Informatics, Год журнала: 2023, Номер 17(1), С. 193 - 209

Опубликована: Ноя. 24, 2023

Язык: Английский

Процитировано