Improving tree species classification using UAS multispectral images and texture measures
| Autor: | Livio Pinto, Giulia Ronchetti, Giovanna Sona, Rossana Gini, D. Passoni |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Multispectral Geography Planning and Development Multispectral image 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION lcsh:G1-922 02 engineering and technology 01 natural sciences Texture (geology) Reduction (complexity) Earth and Planetary Sciences (miscellaneous) Texture Computers in Earth Sciences Accuracy 021101 geological & geomatics engineering 0105 earth and related environmental sciences Planning and Development Vegetation Geography business.industry Orthophoto Ground sample distance Pattern recognition Classification UAS Principal component analysis Artificial intelligence Digital surface business Tree species lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information Volume 7 Issue 8 ISPRS International Journal of Geo-Information, Vol 7, Iss 8, p 315 (2018) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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