Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery.

ABSTRACT: Multispectral satellite imagery offers a new perspective for spatial modelling, change detection and land cover classification. The increased demand for accurate classification of geographically diverse regions led to advances in object-based methods. A novel spatiotemporal method is presented for object-based land cover classification of satellite imagery using a Graph Neural Network. This paper introduces innovative representation of sequential satellite images as a directed graph by connecting segmented land region through time. The method's novel modular node classification pipeline utilises the Convolutional Neural Network as a multispectral image feature extraction network, and the Graph Neural Network as a node classification model. To evaluate the performance of the proposed method

SUBMITTER: Kavran D 

PROVIDER: S-EPMC10384354 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature