Discovering similarities in Landsat satellite images using the K-means method

Patricia, Ariza-Colpas Paola; Carrascal, Ana Isabel Oviedo; De-la-Hoz-Franco, Emiro

doi:10.1016/j.procs.2020.03.017

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Process. e K-means algorithm is an unsupervised learning algorithm that has become one of the most widely used clustering algorithms [28,29]. It is a distance-based clustering algorithm that uses the distance between objects as an evaluation index of similarity.…”

Section: K-means Algorithmmentioning

confidence: 99%

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Liu

Wang

2022

Mathematical Problems in Engineering

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Weight determination aims to determine the importance of different attributes; determining accurate weights can significantly improve the accuracy of classification and clustering. This paper proposes an accurate method for attribute weight determination. The method uses the distance from the sample point of each class to the class center point. It can minimize the weights and determines the attribute weights of the constraints through the objective function. In this paper, the attribute weights obtained by the exact solution are applied to the K-means clustering algorithm; three classic machine learning data sets, the iris data set, the wine data set, and the wheat seed data set, are clustered. Using the normalized mutual information as the evaluation index, a confusion matrix was established. Finally, the clustering results are visualized and compared with other methods to verify the effectiveness of the proposed method. The results show that this method improves the normalized mutual information by 0.11 and 0.08, respectively, compared with the unweighted and entropy weighted methods for iris clustering results. Furthermore, the performance on the wine data set is improved by 0.1, and the performance on the wheat seed data set is improved by 0.15 and 0.05.

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Section: K-means Algorithmmentioning

confidence: 99%

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

Liu

Wang

2022

Mathematical Problems in Engineering

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“…Despite the need for a standard approach to the classification system, none of the current methods has been accepted as the standard, and many approaches exist. Methods of automated image classification include k-means clustering [109], principal component analysis [110], hierarchical clustering [111], segmentation [112], object-based classification, and deep learning approaches [113]. Among these, clustering uses algorithms that group pixels with common characteristics into clusters that represent different land cover types.…”

Section: Discussionmentioning

confidence: 99%

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

Lemenkova

2023

ASI

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Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: `i.cluster’ was used to generate image classes; `i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as `i.group’, `r.support’, `r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa.

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“…The k-means clustering technique was used for unsupervised image classification as a commonly accepted method for remote sensing data processing (Bovolo et al, 2018;Esche and Franklin, 2002;Hou et al, 2016;Paola Patricia et al, 2020). The k-means clustering in image processing by R presents a method of image partition that divides the raster matrix with n pixels (or cells of this matrix) into k groups.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

Lemenkova,

Debeir

2023

Artificial Satellites

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This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8–9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent eco systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa.

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Discovering similarities in Landsat satellite images using the K-means method

Cited by 3 publications

References 14 publications

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

An Accurate Method of Determining Attribute Weights in Distance-Based Classification Algorithms

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

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