Abstract: Due to factors such as tree species and growing environments in forested areas, phenomena such as uneven distribution and holes appear in forest areas within remote sensing images, making it difficult to accurately extract these areas using traditional fuzzy C-means (FCM) clustering algorithms. To address this issue, a superpixel-based fuzzy C-means method for forest areas extraction from remote sensing images is proposed. Firstly, a GAN-based morphological composite filter is employed to fill holes in the remote sensing forest areas images. Secondly, multi-scale morphology is utilized to transform clustering from individual pixels to superpixels, reducing the complexity of the clustering algorithm. Finally, histogram-based  fuzzy C-means clustering is applied to superpixel blocks to extract forest areas information. Experimental results on optical forest areas remote sensing images demonstrate that the proposed algorithm outperforms several other FCM algorithms in terms of performance metrics such as segmentation accuracy, normalized mutual information, F1-score, and Kappa coefficient, with the algorithm achieving a highest accuracy (ACC) of 89.05% and an F1-score of 93.95%.

Key words: forest areas remote sensing image, superpixel, fuzzy C-means clustering, multiscale morphology