用纯光学影像实现冰川地貌精准分割，轻量模型效率更高
MCD-Net: A Lightweight Deep Learning Baseline for Optical-Only Moraine Segmentation
- 采用MobileNetV2+CBAM+DeepLabV3+架构，兼顾精度与效率
- 在3340张图像上达到62.3% mIoU和72.8% Dice系数
- 首次公开光学仅数据集，适合高海拔冰川监测应用
冰川分割对重建古冰川动态和评估气候驱动的地貌变化至关重要。然而，光学对比度弱及高分辨率数字高程模型（DEM）稀缺限制了自动化测绘。本研究构建首个大规模纯光学冰碛物分割数据集，包含3,340张来自谷歌地球的高分辨率图像，覆盖中国四川与云南冰川区。提出MCD-Net轻量基线模型，融合MobileNetV2编码器、卷积块注意力模块（CBAM）与DeepLabV3+解码器。在基准测试中，相比更深的主干网络（如ResNet152、Xception），MCD-Net实现62.3%的平均交并比（mIoU）与72.8%的Dice系数，同时计算成本降低超过60%。尽管脊线提取受亚像素宽度和光谱模糊性制约，结果表明纯光学影像可实现可靠的冰碛体分割。数据集与代码已公开于https://github.com/Lyra-alpha/MCD-Net，为冰碛物分割建立可复现基准，并提供适用于高海拔冰川监测的可部署基线。
原文摘要 · Abstract (English)
Glacial segmentation is essential for reconstructing past glacier dynamics and evaluating climate-driven landscape change. However, weak optical contrast and the limited availability of high-resolution DEMs hinder automated mapping. This study introduces the first large-scale optical-only moraine segmentation dataset, comprising 3,340 manually annotated high-resolution images from Google Earth covering glaciated regions of Sichuan and Yunnan, China. We develop MCD-Net, a lightweight baseline that integrates a MobileNetV2 encoder, a Convolutional Block Attention Module (CBAM), and a DeepLabV3+ decoder. Benchmarking against deeper backbones (ResNet152, Xception) shows that MCD-Net achieves 62.3% mean Intersection over Union (mIoU) and 72.8% Dice coefficient while reducing computational cost by more than 60%. Although ridge delineation remains constrained by sub-pixel width and spectral ambiguity, the results demonstrate that optical imagery alone can provide reliable moraine-body segmentation. The dataset and code are publicly available at https://github.com/Lyra-alpha/MCD-Net, establishing a reproducible benchmark for moraine-specific segmentation and offering a deployable baseline for high-altitude glacial monitoring.
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