Title :
Deep learning to classify difference image for image change detection
Author :
Jiaojiao Zhao ; Maoguo Gong ; Jia Liu ; Licheng Jiao
Author_Institution :
Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi´an, China
Abstract :
Image change detection is a process to analyze multi-temproal images of the same scene for identifying the changes that have occurred. In this paper, we propose a novel difference image analysis approach based on deep neural networks for image change detection problems. The deep neural network learning algorithm for classification includes unsupervised feature learning and supervised fine-tuning. Some samples with the labels of high accuracy obtained by a pre-classification are used for fine-tuning. Since a deep neural network can learn complicated functions that can represent high-level abstractions, it can obtain satisfactory results. Theoretical analysis and experiment results on real datasets show that the proposed method outperforms some other methods.
Keywords :
image classification; neural nets; object detection; unsupervised learning; deep neural network learning algorithm; difference image analysis; difference image classification; high-level abstractions; image change detection; multitemporal image analysis; pre-classification; supervised fine-tuning; unsupervised feature learning; Conferences; Joints; Neural networks;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889510