Classification algorithm for land use in the giant panda habitat of Jiajinshan based on spatial case-based reasoning

Abstract

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Jiajin Mountain, where the giant pandas reside, is an essential nature reserve in China. To comprehend the land use classification of the habitat, this article proposes a remote sensing interpretation algorithm based on spatial case reasoning, known as spatial case-based reasoning (SCBR). The algorithm incorporates specific spatial factors into its framework and does not require an extensive amount of domain knowledge and eliminates the need for a complex model training process, making it capable of completing land use classification in the study area. SCBR comprises a spatial case expression model and a spatial case similarity reasoning model. The paper conducted comparative experiments between the proposed algorithm and support vector machine (SVM), U-Net, vision transformer (ViT), and Trans-Unet, and the results demonstrate that spatial case-based reasoning produces superior classification outcomes. The land use classification experiment based on spatial case-based reasoning at the Jiajinshan giant panda habitat produced satisfactory experimental results. In the comparative experiments, the overall accuracy of SCBR classification reached 95%, and the Kappa coefficient reached 90%. The paper further analyzed the changes in land use classification from 2018 to 2022, and the average accuracy consistently exceeds 80%. We discovered that the ecological environment in the region where the giant pandas reside has experienced significant improvement, particularly in forest protection and restoration. This study provides a theoretical basis for the ecological environment protection of the area.

Keywords

• remote sensing image segmentation
• land use
• case-based reasoning
• spatial features
• machine learning
• Jiajinshan giant panda habitat