Preprint Article Version 1 This version is not peer-reviewed

Spectral-Spatial Dimensionality Reduction of APEX Hyperspectral Imagery for Tree Species Classification; a Case Study of Salzach Riparian Mixed Forest

Version 1 : Received: 11 June 2018 / Approved: 12 June 2018 / Online: 12 June 2018 (10:55:07 CEST)

How to cite: Dabiri, Z.; Lang, S. Spectral-Spatial Dimensionality Reduction of APEX Hyperspectral Imagery for Tree Species Classification; a Case Study of Salzach Riparian Mixed Forest. Preprints 2018, 2018060188 (doi: 10.20944/preprints201806.0188.v1). Dabiri, Z.; Lang, S. Spectral-Spatial Dimensionality Reduction of APEX Hyperspectral Imagery for Tree Species Classification; a Case Study of Salzach Riparian Mixed Forest. Preprints 2018, 2018060188 (doi: 10.20944/preprints201806.0188.v1).

Abstract

Tree species composition is an important key element for biodiversity and sustainable forest management, and hyperspectral data provide detailed spectral information, which can be used for tree species classification. There are two main challenges for using hyperspectral imagery: a) Hughes phenomena, meaning by increasing the number of bands in hyperspectral imagery, the number of required classification samples would increase exponentially, and b) in a more complex environment, such as riparian mixed forest, focusing on spectral variability per pixel may not be adequate for definability of tree species. Therefore, the focus of this study is to assess spectral-spatial dimensionality reduction of airborne hyperspectral imagery by using minim noise fraction (MNF) transformation, and object-based image analysis (OBIA). An airborne prism experiment (APEX) hyperspectral imagery was used. A study area was a riparian mixed forest located along the Salzach river, and six tree species including Picea abies, Populus (canadensis and balsamifera), Fraxinus excelsior, Alnus incana, and Salix alba were selected. A machine learning algorithm random forest (RF) was used to train and apply a prediction model for classification. Using a spectral dimensionality reduced APEX, a pixel-level classification was also done. According to a confusion matrix, the object-level classification of MNF-derived components achieved the overall accuracy of 85 %, and kappa coefficient of 0.805. The performance of classes according to producer’s accuracy varied between 80% for Fraxinus excelsior, Alnus incana, and Populus canadensis to 90% for Salix alba and Picea abies. Comparison the results to a pixel-level classification, showed a better performance of object-level classification (an overall accuracy of 63% and Kappa coefficient of 0.559 were achieved for pixel-level classification). The performance of classes using pixel-based classification varied 45 % for Alnus incana to 80% for Picea abies. In general, Spectral-spatial complexity reduction using MNF transformation and object-level classification yielded a statistically satisfactory results.

Subject Areas

minimum noise fraction (MNF) transformation; object-based image analysis (OBIA); APEX hyperspectral imagery; Random forest (RF) classifier; multiresolution segmentation (MRS); tree species classification

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