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Combining Global Features and Local Interoperability Optimization Method for Extracting and Connecting Fine Rivers

Gorde:
Argitaratua izan da:Remote Sensing vol. 17, no. 5 (2025), p. 742
Egile nagusia: Xu, Jian
Beste egile batzuk: Gao, Xianjun, Wang, Zaiai, Li, Guozhong, Luan, Hualong, Cheng, Xuejun, Yao, Shiming, Wang, Lihua, Shi, Sunan, Xiao, Xiao, Xie, Xudong
Argitaratua:
MDPI AG
Gaiak:
Accuracy
Deep learning
Image resolution
Algorithms
Optimization techniques
Segmentation
Rivers
Remote sensing
Feature selection
Hydrology
Skeleton
Seasonal variations
Network topologies
Machine learning
Regularization
River networks
Topology
Cluster analysis
Image filters
Clustering
Completeness
Water resources
Decision making
Neural networks
Discontinuity
Optimization
Support vector machines
Classification
Image classification
Methods
Image quality
Decision trees
Water bodies
Vector quantization
Resource management
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Laburpena:Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine river features are frequently underrepresented, leading to fragmented and discontinuous water body extraction. To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. By thoroughly analyzing the global features of high-resolution remote sensing images, Frangi filtering is employed to enhance the river linear characteristics. Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. In the next stage, to preserve the original river topology and ensure stripe continuity, a river skeleton refinement algorithm is utilized to retain critical skeletal information about the river networks. Following this, river endpoints are identified using a connectivity domain labeling algorithm, and the bounding rectangles of potential disconnected regions are delineated. To address discontinuities, river endpoints are shifted and reconnected based on structural similarity index (SSIM) metrics, effectively bridging gaps in the river network. Finally, nonlinear water optimization combined K-means clustering segmentation, topology and spectral inspection, and small-area removal are designed to supplement some missed water bodies and remove some non-water bodies. Experimental results demonstrate that the proposed method significantly improves the regularization and completeness of river extraction, particularly in cases of fine, narrow, and discontinuous river features. The approach ensures more reliable and consistent river delineation, making the extracted results more robust and applicable for practical hydrological and environmental analyses.
ISSN:2072-4292
DOI:10.3390/rs17050742
Baliabidea:Advanced Technologies & Aerospace Database