Water body extraction method for high-resolution remote sensing image

Abstract

The invention discloses a water body extraction method for a high-resolution remote sensing image. Firstly, an original high-resolution remote sensing image is given; a local binary pattern and K nearest neighbor-based algorithm is proposed for performing water and land coarse separation; and noise points in a classification area are suppressed through morphological processing. Secondly, a LBP andsupport vector machine-based method is designed for performing further fine separation on a water and land boundary region; and noise points near a refined boundary area are removed through morphological filtering. Finally, for a refinement result, a morphological corrosion operation is adopted for smoothing the edge of a water body to obtain a final water body extraction result. According to themethod provided by the invention, a ''coarse separation and fine separation'' strategy is adopted to carry out water and land separation, so that the accuracy is higher; in addition, two different classification methods of KNN and SVM are adopted, so that on one hand, the extraction efficiency is ensured, and on the other hand, the classification of pixels with different sizes is adapted and a final classification result is steady and effective.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a method for extracting water bodies from high-resolution remote sensing images. Background technique [0002] Water resources are an essential resource for human survival. Real-time and accurate access to water body information is of great significance to the effective management and rational use of water resources. Due to the uneven temporal and spatial distribution of water resources and their fluidity, it is difficult for manual detection to obtain information effectively in real time. With the rapid development of remote sensing technology, the extraction of surface water body information through remote sensing images not only has a wide monitoring range, but also has the advantage of real-time performance. [0003] After years of development of remote sensing technology, the spatial resolution of collected remote sensing images has gradually increased from 10m, 5m, 2m, 1m t...

AI Technical Summary

Problems solved by technology

This invention mainly extracts the water body area based on the spectral characteristics of ground objects, and achieves a certain extraction accuracy, but there are deficiencies: the algorithm needs to preset segmentation parameters, and the parameters of the water body information image in different scenarios need to be reset , and the feature group includes features such as spectrum, topology, shape, and aspect ratio, the process of feature extraction and labeling is complex and time-consuming

[0007] (1) The traditional high-resolution remote sensing image water body extraction method first extracts various types of features of the water body for subsequent water body detection. The feature extraction process is relatively slow, and there may be manual intervention, which reduces the versatility of the algorithm;

[0008] (2) The traditional high-resolution remote sensing image water body extraction method is not ideal when dividing the water body and land boundary area

Images