A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Hocini, Lotfi

doi:10.1109/jsen.2019.2904137

Cited by 41 publications

(24 citation statements)

References 31 publications

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“…It has a vital role in improving the valuation of burned areas, shifting cultivation, monitoring pollution, assessing deforestation, urban growth, and desertification. All over the years, with the imminent need and the availability of data repositories, various methods for change detection have been devised in the remote sensing field [2][3][4]. This work focuses on desertification detection, which is one of the most challenging applications in the LCCD.…”

Section: Introductionmentioning

confidence: 99%

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Zerrouki¹,

Harrou

Zerrouki

et al. 2021

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

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Section: Introductionmentioning

confidence: 99%

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Zerrouki¹,

Harrou

Zerrouki

et al. 2021

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

Self Cite

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“…A huge amount of data gathered from the environment require appropriate analysis to be useful for further analysis and decision making [61]. Machine learning approaches [62] as well as time series analysis [21] may be used to analyze collected data. Urmia lake is located in the span of longitudes of 45 • to 46 • east and latitudes of 37 • to 38.5 • north in the northwest of Iran.…”

Section: Application To Prediction Interval Identification Associated With Urmia Lake Water Level Using Satellite Remote Sensingmentioning

confidence: 99%

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Khanesar

Branson

2021

IEEE Sensors J.

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This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.

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“…Other than determining whether the change happened, it also can be used to determine the authenticity of the change. For the ubiquitous pseudo changes, the weighted RF can act auxiliary tool for other change detection methods by judging the difference between the generated change and the pseudo change [91]. Moreover, different from RF which predicts in parallel, there is a trend to integrate boosting strategy into DT framework, that is, iterating classification results through the cascade of classifiers.…”

Section: • Decision Tree (Dt)mentioning

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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A Machine Learning-Based Approach for Land Cover Change Detection Using Remote Sensing and Radiometric Measurements

Cited by 41 publications

References 31 publications

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

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

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