A Novel Joint Change Detection Approach Based on Weight-Clustering Sparse Autoencoders

Fan, Jianchao; Lin, Kai Yan; Han, Min

doi:10.1109/jstars.2019.2892951

Cited by 18 publications

(7 citation statements)

References 49 publications

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“…However, instead of optimizing the initial centers, many scholars pursue the self-adaption capability. For example, the density-based algorithms [109], which are independent of the initial setting by density adaptation (i.e., Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [110]); the hierarchical clustering algorithms [111,112], which merge clusters with the same criteria level-by-level. Furthermore, in addition to the feature itself, other dimensions also have the possibility for clustering.…”

Section: Methods Of Feature Clusteringmentioning

confidence: 99%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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Quantities of multi-temporal remote sensing (RS) images create favorable conditions for exploring the urban change in the long term. However, diverse multi-source features and change patterns bring challenges to the change detection in urban cases. In order to sort out the development venation of urban change detection, we make an observation of the literatures on change detection in the last five years, which focuses on the disparate multi-source RS images and multi-objective scenarios determined according to scene category. Based on the survey, a general change detection framework, including change information extraction, data fusion, and analysis of multi-objective scenarios modules, is summarized. Owing to the attributes of input RS images affect the technical selection of each module, data characteristics and application domains across different categories of RS images are discussed firstly. On this basis, not only the evolution process and relationship of the representative solutions are elaborated in the module description, through emphasizing the feasibility of fusing diverse data and the manifold application scenarios, we also advocate a complete change detection pipeline. At the end of the paper, we conclude the current development situation and put forward possible research direction of urban change detection, in the hope of providing insights to the following research.

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Section: Methods Of Feature Clusteringmentioning

confidence: 99%

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

2020

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“…Specifically, the most commonly used types of multispectral images for AI-based change detection methods are derived from the Landsat series of satellites and the Sentinel series of satellites [66,67], due to their low acquisition cost and high time and space coverage. In addition, other satellites, such as Quickbird [68][69][70][71][72][73][74], SPOT series [75][76][77][78], Gaofen series [14,79,80], Worldview series [81][82][83][84][85], provide high and very high spatial resolution images, and various aircrafts provide very high spatial resolution aerial images [20,[86][87][88][89][90][91][92][93][94], allowing the change detection results to retain more details of the changes. HSIs have hundreds or even thousands of continuous and narrow bands, which can provide abundant spectral and spatial information.…”

Section: Optical Rs Imagesmentioning

confidence: 99%

“…Thus, it is widely used in change detection tasks as the feature extractor. The commonly used AE models are stacked AEs [97,98,104], stacked denoising AEs [16,101,106,[121][122][123]151,160,188], stacked fisher AEs [189], sparse AEs [80], denoising AEs [102], fuzzy AEs [105], and contractive AEs [99,103]. These AEs preserve spatial information by expanding pixel neighborhoods into vectors, while convolutional AEs are implemented directly through convolution kernels [170,190].…”

Section: Autoencodermentioning

confidence: 99%

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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“…MS images are generally used for CD, and the most often used MS images for deep learning-based CD algorithms are taken from Landsat [56][57][58][59][60][61][62][63] and the Sentinel series [64,65] of satellites because their low collections cost great temporal and spatial coverage. Furthermore, additional satellites, such as QuickBird, SPOT [66][67][68], Gaofen [69,70], and Worldview, provide very high spatial resolution images, while others provide very high spatial resolution aerial [71] images, allowing the CD findings to preserve more details of the changes. The multispectral datasets are classified into wide and local area change datasets.…”

Section: Multi-spectral Imagesmentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

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A Novel Joint Change Detection Approach Based on Weight-Clustering Sparse Autoencoders

Cited by 18 publications

References 49 publications

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios

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

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

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