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

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.

Change Detection in SAR Images Based on Deep Semi-NMF and SVD Networks

“…Finally, the proposed method is compared with closely related methods on three SAR datasets. The methods used for comparison purpose are principal component analysis and k-means clustering (PCAKM) [18], improved FCM algorithm based on Markov random field (MRFFCM) [39], Gabor feature with two-level clustering (GaborTLC) [19], and deep neural networks with MRFFCM preclassification (D_MRFFCM) [22]. The results of PCAKM, MRFFCM, GaborTLC are implemented by using the authors' publicly available code.…”

Classifiers Accuracy Improvement Based on Missing Data Imputation

“…NN models have previously been applied to solve different tasks of radar ESM processing and more recently, many new radar and target recognition systems comprise neural networks as a key classifier [11]. Previous works using a variety of NN architectures and topologies for radar identification, recognition and classification based on ESM data include popular Multilayer Perceptron (MLP), Radial Basis Function (RBF) based NN, Support Vector Machines (SVM), single parameter dynamic search NN, [11][12][13], and deep learning NN [14].…”

“…In [13] the authors investigate the potential of NN (MLP) when used in Forward Scattering Radar (FSR) applications for target classification. In [14] deep NN architectures are employed for SAR images recognition, while in [15] a vector neural network is applied for emitter identification. In many cases the NN are hybridized with …”

Change detection of bitemporal multispectral images based on FCM and D-S theory

“…It can provide monitoring information of change for government and has been applied to many domains such as forestry monitoring, natural diaster monitoring, and the urban development [1,2]. In general, change detection technique can be divided into two main categories: unsupervised [3][4][5][6][7][8][9][10][11][12][13][14] and supervised change detection methods [15,16].…”

“…In the past few years, many pattern recognition algorithms, such as support vector machine [4] and deep learning neural networks [11], have been applied for the change detection of remotely sensed images. In these algorithms, fuzzy c-means (FCM) algorithms, which can get the degree of uncertainty of feature data belonging to each class and expresses the intermediate property of their memberships, have been widely used in the change detection [8,10,12,[21][22][23][24].…”