arXiv：cs.LG· Pann Thinzar Seint, Subas Chhatkuli, Bryan Atwood·· 3 小时前AI 评分25
基于 Sentinel-1 SAR 时间序列的两阶段级联近实时森林异常检测
A Two-Stage Cascade for Near-Real-Time Forest Anomaly Detection from Sentinel-1 SAR Time Series
AI 导读
研究者提出一种两阶段统计-编码器级联系统，利用 Sentinel-1 时间序列实现近实时森林异常检测。该系统先对同季历史基线的 VH 后向散射做自适应稳健统计 z-score 检验，再用稳定森林斑块训练的卷积自编码器隐空间 SSIM 作为确认门，两阶段一致才触发告警。每条告警附带可审计置信度分数和公顷面积估算，并依据重复发生历史划分低/中/高风险等级，可直接对接 MRV 工作流。
正文
Abstract:Tropical forest monitoring is essential for global climate stability and biodiversity preservation. To address the urgent need for rapid, reliable detection of forest loss which is essential for timely intervention against illegal logging, supply chain transparency, land-use governance and carbon market standards, we introduce a two-stage statistics-encoder cascade for near-real-time anomaly detection using Sentinel-1 time series. Our system is designed to overcome two fundamental challenges in remote sensing: the cloud-cover limitations that restrict optical monitoring and seasonal backscatter variation that causes SAR systems to mistake natural moisture changes for forest loss. The architecture integrates two distinct analytical engines to ensure high-fidelity detection: (1) an adaptive, robust-statistics z-score test on co-registered Sentinel-1 VH backscatter, same-season historical baseline and (2) a learned confirmation gate based on the latent-space structural similarity (SSIM) of a convolutional autoencoder trained on stable-forest patches. A candidate disturbance is confirmed as an alert only when both stages agree, and is assigned a confidence score and a Low/Medium/High risk tier from its repeat-occurrence history. The system produces per-alert auditable confidence scores and area-in-hectares estimates directly compatible with Monitoring, Reporting and Verification (MRV) workflows, sustainable forestry management, operational field checks and environmental risk assessments. Beyond its primary application, the model's flexibility allows for critical environmental applications ranging from selective logging to large-scale agricultural encroachment mapping, flood mapping and so on.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02763 [cs.LG] |
| (or arXiv:2610.02763v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02763 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Subas Chhatkuli PhD [view email]
[v1]
Fri, 2 Oct 2026 03:42:39 UTC (4,372 KB)
来源：arXiv：cs.LG · arxiv.org