Paper Info
Title
AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images
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.
Year
DOI
Venue
2019
10.1007/978-3-030-16142-2_29
pacific-asia conference on knowledge discovery and data mining
Field
DocType
Citations 
Change detection,Computer science,Convolutional neural network,Remote sensing,Feature extraction,Contextual image classification,Feature aggregation
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Qiankun Ye110.68
Xiankai Lu2659.78
Hong Huo312617.77
Lihong Wan4123.54
Yiyou Guo542.75
Tao Fang622631.10