ABSTRACT

Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background.

摘要

从多时相遥感影像中进行变化检测是识别森林大火后燃烧区域的有效方法。但是，复杂的图像场景和多光谱带中类似的光谱特征可能会导致许多错误的正误差，这使得难以准确地精确定位燃烧区域。本文提出了一种新颖的燃烧面积变化检测方法。它是基于新的归一化刻录比率SWIR（NBRSWIR）索引和自动阈值算法设计的。该方法的有效性已在三个Landsat-8数据集上得到验证，这些数据集展示了全球范围内的各种火灾事件。与文献中开发的八种基于索引的检测方法相比，该方法在类别可分离性（2.49、1.74和2.06）和准确性（98.93％，98）方面具有最佳性能。57％和99.51％）。同时，它还可以更好地抑制背景中复杂的无关变化。