论文标题
通过异质遥感图像进行靶向变更检测，用于森林死亡率映射
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
论文作者
论文摘要
最近已经开发了几种通用方法，用于在异质遥感数据中进行更改检测，例如合成孔径雷达（SAR）和多光谱辐射仪的图像。但是，这些并不适合检测生态系统某些干扰的弱特征。为了解决这个问题，我们提出了一种基于图像到图像翻译和一级分类（OCC）的新方法。我们的目的是使用多源卫星图像在稀疏的森林森林森林tundra ecotone中爆发的森林死亡率。事件前和之后的图像分别由Landsat-5和Radarsat-2收集。使用最新的深度学习方法用于变更感知图像翻译，我们计算两个卫星各自域中的差异图像。这些差异与原始的事前和事后图像一起堆叠，并传递给对目标变更类别的小样本进行训练的OCC。分类器生成了森林死亡率复杂模式的可靠地图。
Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites' respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality.
