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AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images

  • Qiankun Ye
  • Xiankai Lu
  • Hong Huo
  • Lihong Wan
  • Yiyou Guo
  • Tao FangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

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.

Keywords

Multiple change detection Remote sensing Aggregation network 

Notes

Acknowledgment

This study was partly supported by the National Science and Technology Major Project (21-Y20A06-9001-17/18), the National Key Research and Development Program of China (No. 2018YFB0505000), the National Natural Science Foundation of China (No. 41571402), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 61221003).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qiankun Ye
    • 1
  • Xiankai Lu
    • 1
  • Hong Huo
    • 1
  • Lihong Wan
    • 1
  • Yiyou Guo
    • 1
  • Tao Fang
    • 1
    Email author
  1. 1.Department of Automation, School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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