AggregationNet:Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images
The detection of multiple changes(i.e.,different change types)in bitemporal remote sensing images is a challenging task.Numerous methods focus on detecting the changing location while the detailed “from-to change types are neglected.This paper presents a supervised framework named AggregationNet to identify the specific “from-to change types.This AggregationNet takes two image patches as input and directly output the change types.The AggregationNet comprises a feature extraction part and a feature aggregation part.Deep “from-to features are extracted by the feature extraction part which is a two-branch convolutional neural network.The feature aggregation part is adopted to explore the temporal correlation of the bitemporal image patches.A one-hot label map is proposed to facilitate AggregationNet.One element in the label map is set to 1 and others are set to 0.Different change types are represented by different locations of 1 in the one-hot label map.To verify the effectiveness of the proposed framework,we perform experiments on general optical remote sensing image classification datasets as well as change detection dataset.Extensive experimental results demonstrate the effectiveness of the proposed method.
Multiple change detection Remote sensing Aggregation network
Qiankun Ye Xiankai Lu Hong Huo Lihong Wan Yiyou Guo Tao Fang
Department of Automation,School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,China
国际会议
澳门
英文
375-386
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）