用深度学习与目标分析结合，提升亚马逊雨林森林覆盖制图精度
ForCM: Forest Cover Mapping from Multispectral Sentinel-2 Image by Integrating Deep Learning with Object-Based Image Analysis
- 将深度学习模型与目标图像分析融合，优化森林区域识别
- 最高准确率达95.64%，优于传统方法的92.91%
- 适合遥感、环保监测人员使用，工具开源易用
本研究提出一种名为ForCM的新方法，通过融合基于对象的图像分析（OBIA）与深度学习（DL），利用多光谱Sentinel-2影像进行森林覆盖制图。研究测试了UNet、UNet++、ResUNet、AttentionUNet和ResNet50-Segnet等多种深度学习模型，应用于亚马逊雨林的Sentinel-2 Level 2A高分辨率卫星影像。数据集包含两组三波段和一组四波段影像。经评估后，最优深度学习模型分别与OBIA技术集成以提升制图精度。该工作创新性在于系统比较不同深度学习模型与OBIA结合的效果，并与传统方法对比。结果表明，ForCM方法显著提升精度：ResUNet-OBIA达94.54%，AttentionUNet-OBIA达95.64%，高于传统OBIA的92.91%。研究还验证了QGIS等免费工具在实际应用中的可行性，支持全球环境监测与保护。
原文摘要 · Abstract (English)
This research proposes "ForCM", a novel approach to forest cover mapping that combines Object-Based Image Analysis (OBIA) with Deep Learning (DL) using multispectral Sentinel-2 imagery. The study explores several DL models, including UNet, UNet++, ResUNet, AttentionUNet, and ResNet50-Segnet, applied to high-resolution Sentinel-2 Level 2A satellite images of the Amazon Rainforest. The datasets comprise three collections: two sets of three-band imagery and one set of four-band imagery. After evaluation, the most effective DL models are individually integrated with the OBIA technique to enhance mapping accuracy. The originality of this work lies in evaluating different deep learning models combined with OBIA and comparing them with traditional OBIA methods. The results show that the proposed ForCM method improves forest cover mapping, achieving overall accuracies of 94.54 percent with ResUNet-OBIA and 95.64 percent with AttentionUNet-OBIA, compared to 92.91 percent using traditional OBIA. This research also demonstrates the potential of free and user-friendly tools such as QGIS for accurate mapping within their limitations, supporting global environmental monitoring and conservation efforts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。