JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
  Multi-Teacher Knowledge Distillation Framework
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.
67bb33f3829dedfc99ae1288
Ziyuan Liu
claimed_verified
2025-02-24T09:07:29.223Z
67bb32b6a0cb6e48cfd27d80
/avatars/3cafe3a3fb60405252962d00105667c5.svg
Ziyuan Liu
user
circleLZY
67bb33f3829dedfc99ae1289
Ruifei Zhu
67bb33f3829dedfc99ae128a
Long Gao
67bb33f3829dedfc99ae128b
Yuanxiu Zhou
67bb33f3829dedfc99ae128c
Jingyu Ma
admin_assigned
2025-02-24T16:16:21.974Z
673a9a638127cd120c9c272d
/avatars/d6e225d7a869487cb48c4ac89d048cb4.svg
Jingyu Ma
user
jingyum
67bb33f3829dedfc99ae128d
Yuantao Gu
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
  Multi-Teacher Knowledge Distillation Framework
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.
A multi-teacher knowledge distillation framework improves remote sensing image change detection performance across different models and datasets.
remote sensing image change detection CD JL1-CD dataset SYSU-CD dataset multi-teacher knowledge distillation MTKD framework