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Decision Support System for Hyperspectral Remote-Sensing Data of Yellow River Estuary, China
- Source :
- Scientific Programming, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
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.
- Subjects :
- geography
Markov random field
geography.geographical_feature_category
Article Subject
Hyperspectral imaging
Estuary
Wetland
Markov model
Wetland classification
Computer Science Applications
Support vector machine
QA76.75-76.765
Cohen's kappa
Environmental science
Computer software
Software
Remote sensing
Subjects
Details
- ISSN :
- 1875919X and 10589244
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Scientific Programming
- Accession number :
- edsair.doi.dedup.....f29e1b43ff02fd5ef56f1d338fa87fed