Conifer Species Recognition with Seasonal Hyperspectral Data
不同季相针叶树种高光谱数据识别分析

In situ hyperspectral data obtained with a high spectral resolution radiometer were analyzed for idendfication of six conifer species. Hyperspectral data were measured in the summer and late fall seasons from both the sunlit and . shaded sides of canopies. An artificial neural network algorithm was applied for the identification purpose. Six types of transformation were applied to the hyperspectral data R preprocessed with a simple smoothing followed by band merging. These include log(R), first derivative of R, first derivative of log(R), normalized R, first derivative of normalized R, and log(N(R)). First derivative of log(R) and fist derivative of normalized R resulted in best species recognition accuracies with greater than 94% average accuracies. The effect of hyperspectral data taken from the shade sides of tree canopies can be minimized by applying normalization or by taking derivative after applying logarithm to the preprocessed data. We found that a big difference in solar angle due to seasonality did not cause noticable difference in accumcies of species recognition. A band width of 20nm or narrower is recommended for the recognition of the six species.