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

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Year : 2016 Volume : 27

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS publication information

Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond

Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms

Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection

Learning Subspace-Based RBFNN Using Coevolutionary Algorithm for Complex Classification Tasks

A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting

Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form

Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines

A Further Study on Mining DNA Motifs Using Fuzzy Self-Organizing Maps

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

A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input

Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems

Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances

Convergence Rate for Discrete-Time Multiagent Systems With Time-Varying Delays and General Coupling Coefficients

Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay

Mirror Inverse Operations in Linear Nearest Neighbors Using Dynamic Learning Algorithm

2016 IEEE Symposium Series on Computational Intelligence

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IEEE Transactions on Neural Networks information for authors

Year : 2015 Volume : 26

Year : 2014 Volume : 25

Year : 2013 Volume : 24

Year : 2012 Volume : 23

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

Content Provider	IEEE Xplore Digital Library
Author	Maoguo Gong Jiaojiao Zhao Jia Liu Qiguang Miao Licheng Jiao
Copyright Year	2012
Abstract	This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference image (DI) that shows difference degrees between multitemporal synthetic aperture radar images. Thus, it can avoid the effect of the DI on the change detection results. The learning algorithm for deep architectures includes unsupervised feature learning and supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the relationships between the two images. In addition, the supervised fine-tuning aims at learning the concepts of the changed and unchanged pixels. Experiments on real data sets and theoretical analysis indicate the advantages, feasibility, and potential of the proposed method. Moreover, based on the results achieved by various traditional algorithms, respectively, deep learning can further improve the detection performance.
Page Count	14
File Size	5064776
Starting Page	125
Ending Page	138
File Format	PDF
ISSN	2162237X
Volume Number	27
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Synthetic aperture radar Change detection algorithms Algorithm design and analysis Noise Training Joints synthetic aperture radar (SAR). Deep learning image change detection neural network synthetic aperture radar (SAR)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images

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

Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images

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

A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

Synthetic Aperture Radar (sar) Meets Deep Learning

Wavelet Fusion on Ratio Images for Change Detection in SAR Images

SAR image change detection based on deep denoising and CNN

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

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