Abstract
Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.