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

arXiv: https://arxiv.org/abs/2405.01920 · HTML: https://mlsanity.com/paper/2405.01920/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 67 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Trained Ternary Quantization](https://mlsanity.com/paper/1612.01064/) (2016)
- [Soft Threshold Weight Reparameterization for Learnable Sparsity](https://mlsanity.com/paper/2002.03231/) (2020)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [Towards Unified INT8 Training for Convolutional Neural Network](https://mlsanity.com/paper/1912.12607/) (2019)
- [Mixed Precision Training of Convolutional Neural Networks using Integer Operations](https://mlsanity.com/paper/1802.00930/) (2018)
- [Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks](https://mlsanity.com/paper/1911.09737/) (2019)
- [Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers](https://mlsanity.com/paper/1909.02384/) (2019)
- [TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning](https://mlsanity.com/paper/2103.01542/) (2021)
- [Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection](https://mlsanity.com/paper/2001.04271/) (2020)
- [Unsupervised Image Regression for Heterogeneous Change Detection](https://mlsanity.com/paper/1909.05948/) (2019)
- [NITI: Training Integer Neural Networks Using Integer-only Arithmetic](https://mlsanity.com/paper/2009.13108/) (2020)
- [Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images](https://mlsanity.com/paper/2004.07011/) (2020)
- [A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2102.08041/) (2021)
- [Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusion](https://mlsanity.com/paper/2004.03830/) (2020)
- [The Lighter The Better: Rethinking Transformers in Medical Image Segmentation Through Adaptive Pruning](https://mlsanity.com/paper/2206.14413/) (2022)
- [UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation](https://mlsanity.com/paper/2308.01146/) (2023)
- [MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2210.07601/) (2022)
- [dual unet:a novel siamese network for change detection with cascade differential fusion](https://mlsanity.com/paper/2208.06293/) (2022)

## Built on by (most cited first, 1 of 1)

- [Information-Bottleneck Driven Binary Neural Network for Change Detection](https://mlsanity.com/paper/2507.03504/) (2025)

## Abstract (arXiv)

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.