  Existing satellite remote sensing change detection (CD) methods often crop
original large-scale bi-temporal image pairs into small patch pairs and then
use pixel-level CD methods to fairly process all the patch pairs. However, due
to the sparsity of change in large-scale satellite remote sensing images,
existing pixel-level CD methods suffer from a waste of computational cost and
memory resources on lots of unchanged areas, which reduces the processing
efficiency of on-board platform with extremely limited computation and memory
resources. To address this issue, we propose a lightweight patch-level CD
network (LPCDNet) to rapidly remove lots of unchanged patch pairs in
large-scale bi-temporal image pairs. This is helpful to accelerate the
subsequent pixel-level CD processing stage and reduce its memory costs. In our
LPCDNet, a sensitivity-guided channel pruning method is proposed to remove
unimportant channels and construct the lightweight backbone network on basis of
ResNet18 network. Then, the multi-layer feature compression (MLFC) module is
designed to compress and fuse the multi-level feature information of
bi-temporal image patch. The output of MLFC module is fed into the
fully-connected decision network to generate the predicted binary label.
Finally, a weighted cross-entropy loss is utilized in the training process of
network to tackle the change/unchange class imbalance problem. Experiments on
two CD datasets demonstrate that our LPCDNet achieves more than 1000 frames per
second on an edge computation platform, i.e., NVIDIA Jetson AGX Orin, which is
more than 3 times that of the existing methods without noticeable CD
performance loss. In addition, our method reduces more than 60% memory costs of
the subsequent pixel-level CD processing stage.
