用12通道航拍数据训练模型，实现实时夜间与云层下野火精准定位
Real-Time Wildfire Localization on the NASA Autonomous Modular Sensor using Deep Learning
- 基于多光谱图像构建深度学习模型，实现像素级野火分割
- 在4000+样本上达96%分类准确率、74% IoU和84%召回率
- 适用于夜间、烟雾遮挡场景，适合应急响应与无人机监测
高空多光谱航空影像稀缺且成本高，但对机器学习应用于野火检测等重大问题至关重要。本文基于NASA自主模块化传感器（AMS）的12通道中高空（3-50公里）野火影像，构建了人工标注数据集。该数据集涵盖红外（IR）、短波红外（SWIR）和热成像等多谱段信息，包含20次野火任务的影像，通过随机采样生成超过4000张具有高度多样性的图像，涵盖烟雾/云层遮挡、易混淆假阳性及夜间图像。我们训练两个深度神经网络：一个用于图像分类，另一个用于像素级分割，并融合为实时分割模型，高效定位实时图像流中的活跃野火。模型在测试中达到96%分类准确率、74%交并比（IoU）和84%召回率，优于基于卫星数据和传统色彩规则的方法。研究表明，SWIR、IR与热波段数据对区分火线边界最为关键。代码与数据集已公开。
原文摘要 · Abstract (English)
High-altitude, multi-spectral, aerial imagery is scarce and expensive to acquire, yet it is necessary for algorithmic advances and application of machine learning models to high-impact problems such as wildfire detection. We introduce a human-annotated dataset from the NASA Autonomous Modular Sensor (AMS) using 12-channel, medium to high altitude (3 - 50 km) aerial wildfire images similar to those used in current US wildfire missions. Our dataset combines spectral data from 12 different channels, including infrared (IR), short-wave IR (SWIR), and thermal. We take imagery from 20 wildfire missions and randomly sample small patches to generate over 4000 images with high variability, including occlusions by smoke/clouds, easily-confused false positives, and nighttime imagery. We demonstrate results from a deep-learning model to automate the human-intensive process of fire perimeter determination. We train two deep neural networks, one for image classification and the other for pixel-level segmentation. The networks are combined into a unique real-time segmentation model to efficiently localize active wildfire on an incoming image feed. Our model achieves 96% classification accuracy, 74% Intersection-over-Union(IoU), and 84% recall surpassing past methods, including models trained on satellite data and classical color-rule algorithms. By leveraging a multi-spectral dataset, our model is able to detect active wildfire at nighttime and behind clouds, while distinguishing between false positives. We find that data from the SWIR, IR, and thermal bands is the most important to distinguish fire perimeters. Our code and dataset can be found here: https://github.com/nasa/Autonomous-Modular-Sensor-Wildfire-Segmentation/tree/main and https://drive.google.com/drive/folders/1-u4vs9rqwkwgdeeeoUhftCxrfe_4QPTn?=usp=drive_link
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