An SVM Classification of Tree Species Radiometric Signatures Based on the Leica ADS40 Sensor

This paper focuses on the use of multispectral measurements to classify remotely sensed radiance and reflectance information into three tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst.), and birch (Betula pubescens Ehrh., Betula pendula Roth), using a Support Vector Machine (SVM) algorithm. The features used for the classifier are radiometric involving different viewing angles, but without textural information. At-sensor radiance (ASR) signals used here were obtained using a four-band Leica ADS40-SH52 airborne sensor. The experiments were carried out in a forest area at Hyytiälä, in southern Finland (61°50' N, 24°20' E), which has been widely used for similar purposes, so that detailed tree-level information has been reported previously. The flight was carried out on August 23, 2008. ADS40 ASR measurements can be converted to ground reflectance signatures in two viewing directions using atmosphere and BRDF modeling implemented in Leica XPro 4.2 software. Taking into account the assumptions entailed in the radiometric model, the classification performance of the ground reflectance is evaluated only for the pixel values under sunlit conditions and is compared with the performance of the ASR data. The sunlit and shaded parts of the tree crown were extracted based on the use of LiDAR data for crown shape modeling. The classification results for the real multispectral measurements are compared with the earlier results obtained with simulated Leica ADS40 at-sensor radiance response values which were based on the ground-level high-resolution ground reflectance factor measurements using a single viewing direction. The simulated classification accuracy was 75-79% with the original four bands, while it was up to 85-88%, using the simulated fifth channel. It was found here that the classification accuracy using comparable real ADS40-SH52 four-band data and one viewing angle was 75-79% and increased to 78-82% with two viewing angles. The results show that the best-case classification accuracy with real data can reach 88% if trees are modeled as objects with sunlit and shaded areas, and multiple measurements are available for every tree. The results suggest that ground reflectance estimation with normalization of anisotropic reflectance behavior leads to similar classification performance to ASR data, but can in some cases improve the generalization properties of training data.