Features extraction and land cover classification using Sentinel 2 data

Chairet, Radhia; Salem, Yassine Ben; Aoun, Mohamed

doi:10.1109/sta.2019.8717307

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In many applications,especially in industrial surface inspection,texture analysis is an important issue in image processing, medical imaging [5], content-based image retrieval remote sensing [8], and document segmentation, etc. Since the acquisition of images, orientation can be a problem.…”

Section: Methodsmentioning

confidence: 99%

“…Fabric defect classification has not yet attracted a wide research interest. Also, it is very rare that we have based on the texture of tissue in the automatic visual inspection visualization despite the appearance of new texture methods in recent years such as Features Extracted from Co-occurrence Matrix GLCM [5][6][7], or Local Binary pattern LBP [8,9] or local phase quantization LPQ [10].…”

Section: Introductionmentioning

confidence: 99%

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Texture classification of fabric defects using machine learning

Salem

Abdelkrim

2020

IJECE

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In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

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Section: Methodsmentioning

confidence: 99%