Detection of changes in heterogeneous remote sensing images is vital,
especially in response to emergencies like earthquakes and floods. Current
homogenous transformation-based change detection (CD) methods often suffer from
high computation and memory costs, which are not friendly to edge-computation
devices like onboard CD devices at satellites. To address this issue, this
paper proposes a new lightweight CD method for heterogeneous remote sensing
images that employs the online all-integer pruning (OAIP) training strategy to
efficiently fine-tune the CD network using the current test data. The proposed
CD network consists of two visual geometry group (VGG) subnetworks as the
backbone architecture. In the OAIP-based training process, all the weights,
gradients, and intermediate data are quantized to integers to speed up training
and reduce memory usage, where the per-layer block exponentiation scaling
scheme is utilized to reduce the computation errors of network parameters
caused by quantization. Second, an adaptive filter-level pruning method based
on the L1-norm criterion is employed to further lighten the fine-tuning process
of the CD network. Experimental results show that the proposed OAIP-based
method attains similar detection performance (but with significantly reduced
computation complexity and memory usage) in comparison with state-of-the-art CD
methods.

针对异构遥感图像变化检测中高计算和内存开销的问题，本文提出了一种新的轻量级变化检测方法，该方法利用在线全整数修剪 (OAIP) 训练策略，通过当前的测试数据高效地微调变化检测网络，实验结果显示该方法在性能表现上与最先进的变化检测方法相当，但计算复杂度和内存使用方面显著减少。