Improving change detection methods of SAR images using fractals

Aghababaee, Hossein; Amini, Jalal; Tzeng, Y.C.

doi:10.1016/j.scient.2012.11.006

Cited by 17 publications

(9 citation statements)

References 16 publications

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“…There are many types of artificial neural networks in AI and the mainstream network structures used for change detection are described above. In addition, other networks, such as Hopfield networks [47,48,65,[205][206][207], back propagation networks [42,149,208,209], multilayer perceptrons (MLPs) [70,[210][211][212][213][214], extreme learning machines [215], and self-organizing map (SOM) networks [55,[216][217][218][219][220][221], do not require a large number of training samples to learn high-level abstract features as deep neural networks do, but due to their shallow network structure, low sample size requirements, and easy training process, they are also widely used in change detection tasks and can achieve satisfactory results. Since they can be regarded as traditional machine learning techniques, we will not make more detailed comments here due to space limitations and existing reviews [7,222,223].…”

Section: Other Artificial Neural Network and Ai Methodsmentioning

confidence: 99%

“…To obtain the fusion data from multi-period data, the two most common approaches are using change analysis methods and direct concatenation. Change analysis methods, such as CVA [47], differencing by log-ratio operator [18,148] or change measures [103,149], are able to directly provide change intensity information (i.e., the difference data) in multitemporal data, which can highlight change information and facilitate change detection. The direct concatenation method can retain all the information of the multi-period data, so the change information is extracted by the subsequent classifier.…”

Section: Direct Classification Structurementioning

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: Other Artificial Neural Network and Ai Methodsmentioning

confidence: 99%

Section: Direct Classification Structurementioning

confidence: 99%

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

et al. 2020

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“…The effectiveness of such an operator to suppress speckle noise is limited because it ignores the spatial information. Aghababaee, Amini, and Tzeng (2013) improved the change detection method using the new measures containing a grey level gradient or intensity information and the fractal dimension. Although many features including texture features and spatial information can be used as input of traditional algebraic operation change detection algorithm, the detection accuracy is not high because of the influence of speckle noise.…”

Section: Introductionmentioning

confidence: 99%

A correlation change detection method integrating PCA and multi- texture features of SAR image for building damage detection

Gong

Zhang

2019

European Journal of Remote Sensing

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Synthetic Aperture Radar (SAR) data are used to map affected urban areas after an earthquake generally exploiting change detection techniques. In this paper, a novel change detection approach is proposed for multitemporal (SAR) images. The approach is based on two different parts, which are constructed through principal components (PCs) of multiple textural features and correlation analysis image, respectively. In the extraction of the PCs of texture, the principal component analysis technique is used to separate irrelevant and noisy elements. Then, correlation change detection (CCD) is carried out to detect the changes associated with the building collapse. Considering the earthquake that hit L'Aquila city (Italy) on 6 April 2009, the effectiveness of the proposed method is proved by the experiment results obtained on real SAR images data sets collected before and after the seismic event.

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“…In this regard, there have been a number of researches for PolSAR data classification. Different classifiers such as MLP Neural Networks (Haddadi et al, 2011;Salehi et al, 2014), SVM (Aghababaee, Amini, & Tzeng, 2013;Bai et al, 2014;Chen et al, 2010;Maghsoudi et al, 2012), Fuzzy Inference systems (FIS) (Hellmann & Jäger, 2002), Decision Tree (Qi, Yeh, Li, & Lin, 2012), Random Forest (Du, Samat, Waske, Lie, & Li, 2015), Wishart (Lee, Grunes, & Kwok, 1994) and maximum likelihood (ML) (Lee & Pottier, 2001) have been used as classifier.…”

Section: Introductionmentioning

confidence: 99%

A class-based approach to classify PolSAR imagery using optimum classifier

Safaee

Sahebi

2019

European Journal of Remote Sensing

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Classification of Polarimetric SAR (PolSAR) imagery is still one of the challenges in active remote sensing applications. Although a large number of features and different classifiers have been proposed, no unique approach has been found to satisfy all of the images and classes yet. In this paper, given the extracted features from PolSAR data, a new class-based feature selection (CBFS) algorithm is proposed to find the most optimum features for each class. Maximizing the discrimination of each class from the others is the main contribution of the CBFS which yields distinctive features. The selected features are then employed by classifiers to generate different classification results. Finally, a new approach is developed to combine these classification results to produce the final land cover map. Five different classifiers of Wishart Maximum Likelihood, Gaussian Maximum Likelihood, Support Vector Machine, Multi Layer Perceptron and Fuzzy Inference System are also used for classification. Given the CBFS results, two different Radarsat-2 and AirSAR PolSAR data were classified. Selected features led to improvement of about 5% in producer accuracies in comparison with two well-known Genetic Algorithm Feature Selection (GAFS) and Prototype Space Feature Selection (PSFS) methods. Moreover, Comparison results demonstrate that the fuzzy classifiers could improve the accuracies about 3% if they are suitably constructed and well designed. The achieved higher overall accuracy for the final classified map shows the effectiveness of the proposed approach over the other compared classification procedures.

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Improving change detection methods of SAR images using fractals

Cited by 17 publications

References 16 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

A correlation change detection method integrating PCA and multi- texture features of SAR image for building damage detection

A class-based approach to classify PolSAR imagery using optimum classifier

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