Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China

Wang Ping,Shah Nazir,Muhammad Yasir,Md Sakaouth Hossain,Wenyu Qiao,Jie Fu,Sheng Hui,Rahman Ali

doi:10.1155/2021/1376167

Abstract

There are many wetland resources in the area where the Yellow River enters the sea. This area has good ecological and economic value. Therefore, wetlands are precious resources. The accuracy of traditional wetland classification methods is low (for example, based on the support machine method). In order to explore ways to improve the accuracy of wetland classification, this paper selected the wetland at the mouth of the Yellow River as the study area. And, we used the hyperspectral data of “Zhuhai No. 1” as the research data. Then, we used the logarithmic transformation method to enhance the spectral characteristics of remote-sensing images. Finally, we used Markov random field method (MRF) and support vector machine method (SVM) to finely classify the wetlands in the Yellow River estuary area. We used these experiments to explore wetland classification methods for hyperspectral data. The results showed that the settings of the coupling coefficient and the initial value in the Markov model had a greater impact on the classification results. We found that the best result was when the initial classification number is 50 and the coupling coefficient is 0.5. Compared with the SVM classification method, the overall classification accuracy of our proposed method was improved by 3.9672%, and the Kappa coefficient was improved by 0.042.

Highlights

Wetland, known as the “kidney of the Earth,” is one of the most important ecosystems in the world
In Markov random field, the interaction between variables and the whole can be described by undirected graph model, and it can be used to describe the interaction between pixels in the image. e random variables in Markov are represented as nodes in the undirected graph, and the interaction between the random variables is represented as edges in the undirected graph. e nodes in the undirected graph represent a series of random variables satisfying Markov property, and the edges represent the interdependence between these random variables [44]. e node subset with connecting edges between any two nodes in the undirected graph model has called a group
All bands are divided. e logarithmic transformation can expand the bright area of the image, compress the dark area of the image, and make the variance of different bands different. erefore, more information can be obtained after logarithmic transformation of the image, and the wetland features in the study area are easier to identify and classify

Summary

Introduction

Known as the “kidney of the Earth,” is one of the most important ecosystems in the world. Because they have important biogeochemical, hydrological, and ecological functions, they have high generation and considerable economic and ecological value [1,2,3,4], and they play a key role in mitigating floods and filtering sewage and providing important habitat for many plants and for animals, these systems can greatly affect the human living environment. In the past 50 years, after land cover changes in some areas, wetlands have been polluted and greatly reduced [5]. Wetlands are characterized by complex and lush vegetation, which is usually difficult to map with traditional

Methods

Results

Conclusion