How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests

The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, requiring government or private initiatives for effective forest monitoring. However, identifying deforested regions in satellite images is challenging due to data imbalance, image resolution, low-contrast regions, and occlusion. Superpixel segmentation can overcome these drawbacks, reducing workload and preserving important image boundaries. However, most works for remote-sensing images do not exploit recent superpixel methods. In this work, we evaluate 16 superpixel methods in satellite images to support a deforestation detection system in tropical forests. We also assess the performance of superpixel methods for the target task, establishing a relationship with segmentation methodological evaluation. According to our results, ERS, GMMSP, and DISF perform best on undersegmentation error (UE), boundary recall (BR), and similarity between image and reconstruction from superpixels (SIRSs), respectively, whereas ERS has the best tradeoff with compactness index (CO) and Reg. In classification, SH, DISF, and ISF perform best on RGB, UMDA, and PCA compositions, respectively. According to our experiments, superpixel methods with better tradeoffs among delineation, homogeneity, compactness, and regularity are more suitable for identifying good superpixels for deforestation detection tasks.