融合光学与雷达数据，提升亚马逊雨林树木损失检测精度与鲁棒性。
deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss
- 用卡尔亨托勒展开残差空间生成光学异常图，无需预设数据分布
- 在92×92平方公里区域测试，混合方法准确率超越当前最优方案
- 在多云导致光学数据稀疏时仍表现稳健，适合热带雨林监测
本文提出一种融合光学与合成孔径雷达（SAR）数据的森林砍伐检测流程。核心是基于离散卡亨托勒（KL）展开的残差空间构建光学异常图，利用残差分量的集中界量化异常，该方法不依赖数据分布先验，克服了传统统计参数方法在高维数据中的不适用性。光学异常图与哨兵-1（SAR）和哨兵-2（光学）数据结合，通过隐马尔可夫模型（HMM）分类森林状态。在亚马逊雨林92公里×92公里区域的实验表明，混合光学-雷达方法及纯光学方法均达到高精度，优于现有最先进混合方法；且在光学数据稀疏的多云区域，混合方法显著更鲁棒。
原文摘要 · Abstract (English)
In this paper we develop a deforestation detection pipeline that incorporates optical and Synthetic Aperture Radar (SAR) data. A crucial component of the pipeline is the construction of anomaly maps of the optical data, which is done using the residual space of a discrete Karhunen-Loéve (KL) expansion. Anomalies are quantified using a concentration bound on the distribution of the residual components for the nominal state of the forest. This bound does not require prior knowledge on the distribution of the data. This is in contrast to statistical parametric methods that assume knowledge of the data distribution, an impractical assumption that is especially infeasible for high dimensional data such as ours. Once the optical anomaly maps are computed they are combined with SAR data, and the state of the forest is classified by using a Hidden Markov Model (HMM). We test our approach with Sentinel-1 (SAR) and Sentinel-2 (Optical) data on a $92\,km \times 92\,km$ region in the Amazon forest. The results show that both the hybrid optical-radar and optical only methods achieve high accuracy that is superior to the recent state-of-the-art hybrid method. Moreover, the hybrid method is significantly more robust in the case of sparse optical data that are common in highly cloudy regions.
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