JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
  Multi-Teacher Knowledge Distillation Framework
Authors: name: Ziyuan Liu, name: Ruifei Zhu, name: Long Gao, name: Yuanxiu Zhou, name: Jingyu Ma, name: Yuantao Gu

Deep learning has achieved significant success in the field of remote sensing
image change detection (CD), yet two major challenges remain: the scarcity of
sub-meter, all-inclusive open-source CD datasets, and the difficulty of
achieving consistent and satisfactory detection results across images with
varying change areas. To address these issues, we introduce the JL1-CD dataset,
which contains 5,000 pairs of 512 x 512 pixel images with a resolution of 0.5
to 0.75 meters. Additionally, we propose a multi-teacher knowledge distillation
(MTKD) framework for CD. Experimental results on the JL1-CD and SYSU-CD
datasets demonstrate that the MTKD framework significantly improves the
performance of CD models with various network architectures and parameter
sizes, achieving new state-of-the-art results. The code is available at
https://github.com/circleLZY/MTKD-CD.