A Hybrid Machine Learning Approach for Classifying Aerial Images of Flood-Hit Areas

J. Akshya, P. Priyadarsini
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引用次数: 32

Abstract

Numerous parts of southern India have recently encountered severe damage to lives and properties due to floods. Floods are one among the most destructive natural hazard and recovering to normal life takes ample time. During hazards, various technologies are in use for speeding up relief operations and to minimize the amount of damage, one such being the use of drones. Many algorithms are in need for automatic analysis of remote sensing and aerial images. Nowadays, drones are being used for taking images from varied heights similar to aerial images, as they have cameras with exceptional features and effective sensors. This paper proposes a hybrid approach to classify whether a region in an aerial image is flood affected or not. A combination of Support Vector Machine(SVM) and k-means clustering proved capable of detecting flooded areas with good accuracy, classifying about 92% of flooded images correctly. Performance analysis is done by changing various kernel functions in SVM. The results show that there is a decrease in the prediction and training time when quadratic SVM is used.
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洪涝地区航拍图像分类的混合机器学习方法
印度南部的许多地区最近因洪水遭受了严重的生命和财产损失。洪水是最具破坏性的自然灾害之一,恢复正常生活需要很长时间。在灾害发生期间,人们使用各种技术来加快救援行动,并尽量减少损失,其中一种技术就是使用无人机。为了实现遥感和航空影像的自动分析,需要许多算法。如今,无人机拥有独特的相机和有效的传感器,可以像航空图像一样从不同的高度拍摄图像。本文提出了一种判别航拍图像中某一区域是否受洪水影响的混合方法。结果表明,支持向量机(SVM)与k-means聚类相结合能够很好地检测洪水区域,对约92%的洪水图像进行了正确分类。通过改变支持向量机的各种核函数来进行性能分析。结果表明,使用二次支持向量机可以减少预测时间和训练时间。
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