Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training

• URL: http://arxiv.org/abs/2405.01920v1
• Date: Fri, 3 May 2024 08:23:39 GMT
• Title: Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training
• Authors: Chengyang Zhang, Weiming Li, Gang Li, Huina Song, Zhaohui Song, Xueqian Wang, Antonio Plaza,
• Abstract summary: Current homogenous-based change detection methods often suffer from high computation and memory costs. This paper proposes a new lightweight CD method that employs the online all-integer pruning (OAIP) training strategy to efficiently fine-tune the CD network. Experimental results show that the proposed OAIP-based method attains similar detection performance (but with significantly reduced complexity and memory usage) in comparison with state-of-the-art CD methods.
• Score: 17.387154380465294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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