新基准+多教师蒸馏，提升遥感变化检测精度与鲁棒性
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
- 按变化面积比划分数据，训练多个专用教师模型
- 蒸馏多教师知识，学生模型在多种变化场景下性能提升
- 在吉林一号挑战赛中排名前列，适配各类模型架构
遥感图像变化检测在地球观测中至关重要，但高质量开源数据集稀缺、跨变化类型性能不稳定仍是主要挑战。为此，我们提出JL1-CD——一个包含5000对子米级图像的大规模基准数据集，并设计一种新型原初划分（O-P）策略，将训练集按变化面积比（CAR）划分，为每类子集训练专用教师模型。进一步构建多教师知识蒸馏（MTKD）框架，从多个教师中提取互补知识并融合至单一学生模型，实现无需额外推理开销的跨场景性能提升。MTKD在2024年‘吉林一号’杯挑战赛中获初赛第一、决赛第二。在JL1-CD和SYSU-CD数据集上的大量实验表明，该框架可稳定提升各类网络结构与参数规模模型的检测性能，刷新当前最优结果。代码与数据集见https://github.com/circleLZY/MTKD-CD。
原文摘要 · Abstract (English)
Change detection (CD) in remote sensing images plays a vital role in Earth observation. However, the scarcity of high-resolution, comprehensive open-source datasets and the difficulty in achieving robust performance across varying change types remain major challenges. To address these issues, we introduce JL1-CD, a large-scale, sub-meter CD dataset consisting of 5,000 image pairs. We further propose a novel Origin-Partition (O-P) strategy and integrate it into a Multi-Teacher Knowledge Distillation (MTKD) framework to enhance CD performance. The O-P strategy partitions the training set by Change Area Ratio (CAR) and trains specialized teacher models on each subset. The MTKD framework then distills complementary knowledge from these teachers into a single student model, enabling improved detection results across diverse CAR scenarios without additional inference cost. Our MTKD approach demonstrated strong performance in the 2024 ``Jilin-1'' Cup challenge, ranking first in the preliminary and second in the final rounds. Extensive experiments on the JL1-CD and SYSU-CD datasets show that the MTKD framework consistently improves the performance of CD models with various network architectures and parameter sizes, establishing new state-of-the-art results. Code and dataset are available at https://github.com/circleLZY/MTKD-CD.
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