Enhancement of an Algorithm for Oil Tank Detection in Satellite Images

Jivane, Niketan; Soundrapandiyan, Rajkumar

doi:10.22266/ijies2017.0630.24

Cited by 9 publications

(1 citation statement)

References 10 publications

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“…Jivane and Soundrapandiyan [Jivane and Soundrapandiyan (2017)] used SURF and SVM to recognize oil tanks in satellite images. For linear multi-class classification of building photos, Varma and Rao [Varma and Rao (2017)] integrated generic techniques with SVM.…”

Section: Relevant Workmentioning

confidence: 99%

Satellite Image Classification: From Handcrafted Features to Deep Learning Features

Joshi¹,

Patel²

2021

INDJCSE

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Every day, satellites capture huge volumes of data and monitor the earth's surface. This is a timeconsuming process to manually classify the images into their appropriate classes. A classification system that automatically classifies the images into the proper classes is required. In this article, we have used handcrafted features and deep learning features to classify the satellite images. As handcrafted features, HLAC features have been used and for deep learning features ResNet-50 and ResNet-101 models have been used. This article provides a method for detecting HLAC features in multi-class satellite images and automatically classifying them into their appropriate classes. To classify an image, the image's features are first extracted, and then classification is performed utilizing those features. An SVM classifier is used to classify the images. The Sentinel-2 satellite images from the EuroSAT dataset were used. The accuracy and F1-score were used to assess the performance of the classifier. The SVM classifier with HLAC feature extraction had an F1-score of 86.26 and an accuracy of 89.35%. The dataset's classification accuracy was also assessed using a deep learning model. We obtained a 98.69% accuracy using deep learning models which is 0.12% higher than the benchmarked.

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Section: Relevant Workmentioning

confidence: 99%