Comparison of Spectral Indices and Principal Component Analysis for Differentiating Lodged Rice Crop from Normal Ones

Hyperspectral reflectance of normal and lodged rice caused by rice brown planthopper and rice panicle blast was measured at the canopy level. Over one decade broadand narrow-band vegetation indices (VIs) were calculated to simulate Landsat ETM+ with in situ hyperspectral reflectance. Principal component analysis (PCA) was utilized to obtain the front two principal components (PCs). Probabilistic neural network (PNN) was employed to classify the lodged and normal rice with VIs and PCs as the input vectors. PCs had 100% of overall accuracy and 1 of Kappa coefficient for the training dataset. While PCs had the greatest average overall accuracy (97.8%) and Kappa coefficient (0.955) for the two testing datasets than VIs consisting of broadand narrow-bands. The results indicated that hyperspectral remote sensing with PCA and artificial neural networks could potentially be applied to discriminate lodged crops from normal ones at regional and large spatial scales.