论文标题
使用深度学习分析南美野火的多光谱卫星图像
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning
论文作者
论文摘要
由于频繁的严重干旱正在延长亚马逊雨林中的旱季，因此及时检测野火并预测可能传播可能的传播以有效抑制反应非常重要。目前的野火检测模型对于南美热点的低技术条件的通用性不足。这项深入学习的研究首先使用绿色和短波红外频段来预测像素级二进制消防口罩，在Landsat上训练了8张厄瓜多尔和加拉帕戈斯的完全卷积神经网络。该模型在圭亚那和苏里南的测试数据上获得了0.962验证F2得分和0.932 F2得分。之后，使用K均值聚类在卷曲频段上进行图像分割，以将连续像素值简化为三个离散类，代表不同程度的cirrus cloud污染。训练了三个额外的卷积神经网络，以进行灵敏度分析，以测量简化特征对模型准确性和火车时间的影响。与在原始cirrus图像上训练的对照模型相比，在分段的卷卷图像上训练的实验模型在统计上有显着减少，而不会损害二进制精度。这种概念证明表明，功能工程可以通过降低计算费用来改善野火检测模型的性能。
Since frequent severe droughts are lengthening the dry season in the Amazon Rainforest, it is important to detect wildfires promptly and forecast possible spread for effective suppression response. Current wildfire detection models are not versatile enough for the low-technology conditions of South American hot spots. This deep learning study first trains a Fully Convolutional Neural Network on Landsat 8 images of Ecuador and the Galapagos, using Green and Short-wave Infrared bands to predict pixel-level binary fire masks. This model achieves a 0.962 validation F2 score and a 0.932 F2 score on test data from Guyana and Suriname. Afterward, image segmentation is conducted on the Cirrus band using K-Means Clustering to simplify continuous pixel values into three discrete classes representing differing degrees of cirrus cloud contamination. Three additional Convolutional Neural Networks are trained to conduct a sensitivity analysis measuring the effect of simplified features on model accuracy and train time. The Experimental model trained on the segmented cirrus images provides a statistically significant decrease in train time compared to the Control model trained on raw cirrus images, without compromising binary accuracy. This proof of concept reveals that feature engineering can improve the performance of wildfire detection models by lowering computational expense.
