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.
Abstract (translated)
检测异质遥感图像中的变化对地震和水灾等紧急情况至关重要。目前基于同质变换的变形检测（CD）方法通常导致计算和内存成本较高，这对卫星上的车载CD设备来说并不友好。为解决这个问题，本文提出了一种用于异质遥感图像的新型轻量级CD方法，该方法采用在线所有整数平展（OAIP）训练策略来有效地对CD网络进行微调，利用当前测试数据。所提出的CD网络由两个视觉几何组（VGG）子网络作为基本架构。在OAIP基于训练过程中，所有权重、梯度和中间数据都被量化为整数，以加速训练并减少内存消耗，其中每层模块指数缩放方案被用于减少由于量化引起的网络参数计算误差。其次，采用L1范数 criterion 的自适应滤波器级别剪枝方法进一步减轻了微调过程。实验结果表明，与最先进的CD方法相比，基于OAIP的轻量级方法在检测性能上具有相似的效果（但计算复杂性和内存消耗大大降低）
URL
https://arxiv.org/abs/2405.01920
