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

Samadi, Farnaam; Akbarizadeh, Gholamreza; Kaabi, Hooman

doi:10.1049/iet-ipr.2018.6248

Cited by 123 publications

(58 citation statements)

References 23 publications

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“…The first stage, i.e., preclassification, is usually simple but worth studying, and most of them are unsupervised methods, which can be implemented with difference analysis and clustering [101], such as K-means [162], fuzzy c-means (FCM) [90,99,100,111,151,160,165,[178][179][180][181], spatial FCM [102,154], or hierarchical FCM [21,113]. This stage in some works are implemented by threshold analysis [18,39], saliency analysis [78], or well-designed rules [38,83,84,124,148,182,183]. After obtaining high-confidence changed or/and unchanged samples, the AI model can be trained in a supervised manner for change detection in the second stage.…”

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

“…However, its units within the same layer are not connected to each other and each hidden layer serves as the visible layer for the next. As a feature extractor, it can be trained greedily, i.e., one layer at a time, and appears in many unsupervised change detection methods [23,37,157,183]. On the other hand, the deep Boltzmann machine (DBM), as a graph similar to DBN but undirected, can also achieve such a function [182].…”

Section: Deep Belief Networkmentioning

confidence: 99%

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Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

Section: Deep Belief Networkmentioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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“…S YNTHETIC aperture radar (SAR) systems are capable of working under various seasons and weather conditions [1], [2]. They have been widely used for applications including environmental surveillance and regional planning [3]. With the rapid development of remote sensing technologies, a large number of SAR images are now available.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Zhang

Liu

Jiao

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Patch-level labels are easy to achieve, and they require less expertise and lower resource consumption than pixellevel ones. Each patch has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this paper proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. The structural constraint enforces that the semantically similar features usually co-occur in similar images with a high probability. Finally, each image patch is quantified by discriminative latent semantic topics instead of lowlevel representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

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“…Deep learning has a good application in the field of image denoising algorithms. For example, the supervised deep learning method based on deep belief network (DBN) was used to detect changes in synthetic aperture radar (SAR) images [13], a neural network with hybrid algorithm of CNN and multilayer perceptron (CNN-MLP) was suggested for image classification [14], and so on. In addition, image segmentation is the key step from image processing to image analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

Xiao

Shi

et al. 2020

Computational Intelligence and Neuroscience

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The Mahalanobis–Taguchi system (MTS) is a multivariate data diagnosis and prediction technology, which is widely used to optimize large sample data or unbalanced data, but it is rarely used for high-dimensional small sample data. In this paper, the optimized MTS for the classification of high-dimensional small sample data is discussed from two aspects, namely, the inverse matrix instability of the covariance matrix and the instability of feature selection. Firstly, based on regularization and smoothing techniques, this paper proposes a modified Mahalanobis metric to calculate the Mahalanobis distance, which is aimed at reducing the influence of the inverse matrix instability under small sample conditions. Secondly, the minimum redundancy-maximum relevance (mRMR) algorithm is introduced into the MTS for the instability problem of feature selection. By using the mRMR algorithm and signal-to-noise ratio (SNR), a two-stage feature selection method is proposed: the mRMR algorithm is first used to remove noise and redundant variables; the orthogonal table and SNR are then used to screen the combination of variables that make great contribution to classification. Then, the feasibility and simplicity of the optimized MTS are shown in five datasets from the UCI database. The Mahalanobis distance based on regularization and smoothing techniques (RS-MD) is more robust than the traditional Mahalanobis distance. The two-stage feature selection method improves the effectiveness of feature selection for MTS. Finally, the optimized MTS is applied to email classification of the Spambase dataset. The results show that the optimized MTS outperforms the classical MTS and the other 3 machine learning algorithms.

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

Cited by 123 publications

References 23 publications

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification

Optimized Mahalanobis–Taguchi System for High-Dimensional Small Sample Data Classification

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