SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas

Bekkari, Aissam; Idbraim, Soufiane; Elhassouny, Azeddine; Mammass, Driss; Yassa, Mostafa El; Ducrot, Danielle

doi:10.1007/978-3-642-31254-0_3

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…A significant part of these features was developed by Haralick et al [1,2], thus the indicators are often referred to as Haralick features. Various authors propose the use of various Haralick features [10][11][12]. The effectiveness of this popular method has been demonstrated in a significant number of publications [22,23].…”

Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

“…For example, urban and bare soil areas share similar spectral characteristics, as do forests and areas of low vegetation. As the research shows, the use of textural information in classification, apart from spectral data, can significantly increase the accuracy of classification [1][2][3][4][5][6][7][8][9][10][11][12]. The best results can be obtained by using a combination of spectral and textural data [7,8,12].…”

Section: Introductionmentioning

confidence: 99%

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The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery

Kupidura

2019

Remote Sensing

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The paper presents a comparison of the efficacy of several texture analysis methods as tools for improving land use/cover classification in satellite imagery. The tested methods were: gray level co-occurrence matrix (GLCM) features, Laplace filters and granulometric analysis, based on mathematical morphology. The performed tests included an assessment of the classification accuracy performed based on spectro-textural datasets: spectral images with the addition of images generated using different texture analysis methods. The class nomenclature was based on spectral and textural differences and included the following classes: water, low vegetation, bare soil, urban, and two (coniferous and deciduous) forest classes. The classification accuracy was assessed using the overall accuracy and kappa index of agreement, based on the reference data generated using visual interpretation of the images. The analysis was performed using very high-resolution imagery (Pleiades, WorldView-2) and high-resolution imagery (Sentinel-2). The results show the efficacy of selected GLCM features and granulometric analysis as tools for providing textural data, which could be used in the process of land use/cover classification. It is also clear that texture analysis is generally a more important and effective component of classification for images of higher resolution. In addition, for classification using GLCM results, the Random Forest variable importance analysis was performed.

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Section: Gray Level Co-occurrence Matrix (Glcm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery

Kupidura

2019

Remote Sensing

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“…In previous studies, the texture characteristics of Haralick features were used to extract features in high-resolution satellite imagery. This study resulted in an accuracy rate of 93.29% [12].…”

Section: A R T I C L E I N F Omentioning

confidence: 90%

“…Based on previous research, the k-nearest neighbor method is a method that has a high level of time efficiency and uses less memory [14]. Also, the k-nearest neighbor method produces higher accuracy values compared to the SVM method in the classification using Haralick's texture features based on previous research [12].…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Widodo¹,

Hernando

Mahmudy

2019

Sig.Img.Proc.Lett

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Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

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“…The use of textural information in image classification, apart from spectral data, can significantly increase the accuracy of classification (Haralick et al 1973). The best results can be obtained by using a combination of spectral and textural data (Bekkari et al 2012;Kupidura 2019). Texture can be a distinctive feature of selected land cover classes exhibiting significant spectral similarities.…”

Section: Texture Analysismentioning

confidence: 99%

Monitoring of post-fire forest scars in Serbia based on satellite Sentinel-2 data

Brovkina

Stojanović

Milanović

et al. 2020

Geomatics, Natural Hazards and Risk

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SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas

Cited by 12 publications

References 13 publications

The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery

The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Monitoring of post-fire forest scars in Serbia based on satellite Sentinel-2 data

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