Year: 2024
Type: conference-paper
Abstract: This paper presents the optimization technique to reduce the geospatial data cube size and enhance the land cover classification. The technique is based on training sample separability. Accordingly, the Separability Index of the Training Sample (SITS) was developed and used as the object function for the optimization. In order to test the effectiveness of the optimization technique, the experiment was conducted. It implied the land cover classification of the highly heterogeneous natural landscapes in the case of the Shatsky National Natural Park, where the prevailing landscape is wetlands. After the optimization of the input geospatial data cube, classification enhancement was evidenced by increasing indicators such as overall accuracy by 0.04 from 0.9 to 0.94 and the kappa coefficient by 0.06 from 0.86 to 0.92. In addition, the data cube size was reduced by 5.55 times from 222 to 40 layers
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7166299481