Cited by HistSegNet: Histogram Layered Segmentation Network for SAR Image Based Flood Segmentation

Flood Monitoring Based on Multi-Source Remote Sensing Data Fusion Driven by HIS-NSCT Model DOI

P. F. Ding, Rong Li, Chenfei Duan

et al.

Water, Journal Year: 2025, Volume and Issue: 17(3), P. 396 - 396

Published: Jan. 31, 2025

Floods have significant impacts on economic development and cause the loss of both lives property, posing a serious threat to social stability. Effectively identifying evolution patterns floods could enhance role flood monitoring in disaster prevention mitigation. Firstly, this study, we utilized low-cost multi-source multi-temporal remote sensing construct an HIS-NSCT fusion model based SAR optical order obtain best image. Secondly, constructed regional growth accurately identify floods. Finally, extracted analyzed extent, depth, area farmland submerged by flood. The results indicated that maintained spatial characteristics spectral information images well, as determined through subjective objective multi-index evaluations. Moreover, preserve detailed features water body edges, eliminate misclassifications caused terrain shadows, enable effective extraction bodies. Based Poyang Lake, incorporating precipitation, elevation, cultivated land, other data, accurate identification inundation range, inundated land can be achieved. This study provides data technical support for identification, control, relief decision-making, among aspects.

Language: Английский

Citations

SD-Mamba: A lightweight synthetic-decompression network for cross-modal flood change detection DOI

Yu Shen, Shuang Yao,

Zhenkai Qiang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104409 - 104409

Published: Feb. 1, 2025

Language: Английский

Citations

Evaluating Flood Extent Using Synthetic Aperture Radar (SAR) and Modified Normalized Difference Water Index (MNDWI) Methods DOI

Getu Tessema Tassew,

Addisalem Bitew Mitiku,

Tewodros Mulu Mekonnen

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

Citations

Flood Index-Enhanced deep learning model for coastal inundation mapping in SAR imagery DOI

W Chen, Yinfei Zhou, Xiaofeng Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104550 - 104550

Published: April 19, 2025

Language: Английский

Citations

AP-PointRend: An Improved Network for Building Extraction via High-Resolution Remote Sensing Images DOI

Bowen Zhu, Yu Ding, Xiongwu Xiao

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1481 - 1481

Published: April 22, 2025

The automatic extraction of buildings from remote sensing images is crucial for various applications such as urban planning and management, emergency response, map making updating. In recent years, deep learning (DL) methods have made significant progress in this field. However, due to the complex diverse structures their interconnections, accuracy extracted remains insufficient high-precision maps navigation. To address issue enhancing building boundary extraction, we propose a modified instance segmentation model, AP-PointRend (Adaptive Parameter-PointRend), improve performance extraction. Specifically, model can adaptively select number iterations points based on size large buildings. By introducing regularization constraints, discrete small patches are removed, preserving boundaries better during process. We also designed an image merging method eliminate seams, ensure recall rate, accuracy. Vaihingen WHU benchmark datasets were used evaluate method. experimental results showed that proposed approach generated compared with other state-of-the-art methods.

Language: Английский

Citations

Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria DOI

Lukumon Olaitan Lateef, Hugo Costa, Pedro Cabral

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 384, P. 125542 - 125542

Published: May 1, 2025

Flooding has increasingly significant adverse effects on global food security, and there is a lack of framework to effectively integrate remote sensing with survey data for accurate damage recovery assessment. Also, optical satellite images flood mapping face cloud interference, free synthetic aperture radar (SAR) the temporal frequency needed capture flooding dynamics. This study developed new modelling crop damage, loss, due flash using time-series multi-sensor images. Crop from was validated extensive participatory data. were assessed during Nigeria's 2020 2022 floods. Consistency found between farmer-reported losses sensing-based assessments: 91 % farmers reporting total loss had no recovery. Flood maps assessments achieved over 90 accuracy, demonstrating reliability multi-source SAR combined machine learning technique. Severe evident, only 13 16 flooded cropland recovered in 2022, respectively. The integrated approach this eliminates uncertainties other techniques, overcomes limitations, offers scalability national-level implementation, providing critical information post-disaster planning, farmer compensation, sustainable agricultural practices enhance security changing climate.

Language: Английский

Citations

Flood Extent Mapping in SAR Images using Semi-Supervised Approach DOI

S Girisha,

G. Savitha,

P. Sughosh

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105304 - 105304

Published: May 1, 2025

Language: Английский

Citations

HistSegNet: Histogram Layered Segmentation Network for SAR Image Based Flood Segmentation DOI

İlter Türkmenlı, Erchan Aptoula, Koray Kayabol

et al.

IEEE Geoscience and Remote Sensing Letters, Journal Year: 2024, Volume and Issue: 21, P. 1 - 5

Published: Jan. 1, 2024

Language: Английский

Citations