论文标题
使用Prisma检测甲烷羽流：深度学习模型和数据增强
Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation
论文作者
论文摘要
新一代的高光谱成像仪（例如Prisma）显着提高了我们从高空间分辨率（30m）的空间中甲烷（CH4）羽流的检测能力。我们在这里提出了一个完整的框架，可以使用Prisma卫星任务中的图像和一个能够检测大面积羽毛的深度学习模型来识别CH4羽流。为了弥补Prisma图像的相对稀缺性，我们通过将高分辨率羽流从Sentinel-2转移到Prisma来训练我们的模型。因此，我们的方法可以通过产生广泛而逼真的训练数据库来避免从大型涡流模拟中产生计算昂贵的合成羽流，并为使用未来的高光谱传感器（ENMAP，EMIT，EMIT，CarbonMapper）大规模检测甲烷羽流铺平了道路。
The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning model able to detect plumes over large areas. To compensate for the relative scarcity of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology thus avoids computationally expensive synthetic plume generation from Large Eddy Simulations by generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).
