Abstract
This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User’s and Producer’s Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.
Highlights
Unmanned Aircraft Systems (UAS) are rapidly evolving technologies that are nowadays used in a wide range of geospatial surveys and natural resources management applications
This study investigated whether the use of texture features, derived from ultra-high resolution UAS imagery, can improve the accuracy of vegetation classification
In this paper a workflow based on Remote Sensing standard methods was experimented
Summary
Unmanned Aircraft Systems (UAS) are rapidly evolving technologies that are nowadays used in a wide range of geospatial surveys and natural resources management applications. The UAS available differ in dimension, shape, payload, flight height, and duration. The attention of the most of operators is mainly focused on mini and micro UAS, because of their easiness of use and versatility. The low operational altitude of UAS surveys results in the generation of ultra-high resolution data [5], while their reduced physical size allows their rapid deployment, improving their capability to exploit limited windows of opportunity [6]. The low cost of platforms and navigation systems together with the variety of available sensors make the UAS suitable to be employed in many situations where a traditional vehicle (i.e., airplane) would be too expensive to justify its use
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