对比16种超像素方法，找出最适合热带雨林砍伐检测的算法。
How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests
- 评估16种超像素算法在卫星图像中的表现，选优适配森林监测任务。
- ERS、GMMSP、DISF分别在不同指标下表现最佳，综合性能最优为ERS。
- 结果可指导遥感图像分析中如何选择高质量超像素以提升检测精度。
热带森林保护具有重大的社会与生态意义，但每年数百万公顷遭受砍伐和退化，亟需有效的监测手段。然而，卫星图像中因数据不平衡、分辨率低、对比度弱及遮挡等问题，导致识别困难。超像素分割可减轻工作量并保持重要边界信息。本文评估了16种超像素方法在热带雨林卫星图像上的表现，用于支持砍伐检测系统。结果显示，ERS、GMMSP和DISF在UE、BR和SIRS指标上表现最优，其中ERS在轮廓（CO）与区域一致性（Reg）间平衡最佳。分类任务中，SH、DISF和ISF分别在RGB、UMDA和PCA组合下表现最好。实验表明，兼具边缘刻画、均匀性、紧凑性与规则性的超像素方法更适用于砍伐检测任务。
原文摘要 · Abstract (English)
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 UE, BR, and SIRS, respectively, whereas ERS has the best trade-off with 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 trade-offs between delineation, homogeneity, compactness, and regularity are more suitable for identifying good superpixels for deforestation detection tasks.
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