Sep 28, 2017

Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery

Sensors
Juan SandinoFelipe Gonzalez

Abstract

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is "resolution-dependent". These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imager...Continue Reading

Mentioned in this Paper

Support Vector Machines
Biologic Segmentation
Positioning Attribute
Area
Classification
Intelligence
Optical Image Reconstruction
Knowledge Representation (Computer)
Genetic Vectors
Remote Sensing

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