NDLI: Change detection in SAR images using deep belief network: a new training approach based on morphological images

Content Provider	IET Digital Library
Author	Samadi, Farnaam Akbarizadeh, Gholamreza Kaabi, Hooman
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.
Starting Page	2255
Ending Page	2264
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6248
Journal	IET Image Processing
Publisher Date	2019-05-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Appropriate Data Volume Belief Network Change Detection Map Change Detection Problem Computer Vision And Image Processing Technique Dataset Deep Architecture Deep Belief Network Deep Learning-based Algorithm Deep Learning-based Supervised Method Detection Performance Diversity Image Classification Input Image Input SAR Image Introduced Method Knowledge Engineering Technique Labelled Data Learning in AI Morphological Image Optical, Image And Video Signal Processing Radar Equipment Radar Imaging Supervised Counterparts Supervised Network Fine-tuning Synthetic Aperture Radar Synthetic Aperture Radar Image Changes System And Application Trained DBN Training Approach Training Process Unsupervised Learning Unsupervised Method
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks

Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

SAR image change detection based on deep denoising and CNN

Improved SSD-based transmission tower detection in SAR images

Synthetic aperture radar target recognition of incomplete training datasets via Siamese network

Spectral clustering based unsupervised change detection in SAR images

Object-based method for optical and SAR images change detection

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection in SAR images using deep belief network: a new training approach based on morphological images

Similar Documents

Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks

Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

SAR image change detection based on deep denoising and CNN

Improved SSD-based transmission tower detection in SAR images

Synthetic aperture radar target recognition of incomplete training datasets via Siamese network

Spectral clustering based unsupervised change detection in SAR images

Object-based method for optical and SAR images change detection

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection in SAR images using deep belief network: a new training approach based on morphological images