Paper Info
Title
Change Detection in SAR Images using Deep Belief Network: a New Training Approach based on Morphological Images
Abstract
In solving change detection problem, unsupervised methods are usually preferred to their supervised counterparts due to the difficulty of producing labelled data. Nevertheless, in this paper, a supervised deep learning-based method is presented for change detection in synthetic aperture radar (SAR) images. A Deep Belief Network (DBN) was employed as the deep architecture in the proposed method, and the training process of this network included unsupervised feature learning followed by supervised network fine-tuning. From a general perspective, the trained DBN produces a change detection map as the output. Studies on DBNs demonstrate that they do not produce ideal output without a proper dataset for training. Therefore, the proposed method in this study provided a dataset with an appropriate data volume and diversity for training the DBN using the input images and those obtained from applying the morphological operators on them. The great computational volume and the time-consuming nature of simulation are the drawbacks of deep learning-based algorithms. To overcome such disadvantages, a method was introduced to greatly reduce computations without compromising the performance of the trained DBN. Experimental results indicated that the proposed method had an acceptable implementation time in addition to its desirable performance and high accuracy.
Year
DOI
Venue
2019
10.1049/iet-ipr.2018.6248
Iet Image Processing
Keywords
Field
DocType
belief networks,image classification,synthetic aperture radar,unsupervised learning,radar imaging,learning (artificial intelligence)
Computer vision,Change detection,Pattern recognition,Deep belief network,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
13
12
1751-9659
Citations 
PageRank 
References 
2
0.37
0
Authors
3
Name
Order
Citations
PageRank
Farnaam Samadi120.37
Gholamreza Akbarizadeh2516.19
Hooman Kaabi341.41