IHS transform and low pass filter based image enhancement for crop classification

One of the main task in agricultural monitoring using remote sensing is to estimate crop area through image classification. But sometimes the classification results are not satisfactory resulting from the spectral difference of the same variety of crops for their different growth conditions. In the image the spectral difference of the same crops is like noise and gets more difficult in post classification processing. An image smoothing model (SMM) is developed to improve the clasification. SMM first transforms the multispectral image into intension(I), hue(H) and saturation(S); then a convolution filter is used only on its hue and saturation to get new hue(H′) and saturation(S′) components. The original I, the new H′and S′are together used to get a new RGB image by inverse IHS transformation. Therefore, SMM model preserves the spatial resolution when smoothing a multispectral image. The enhanced image using SMM model proves to be able to obtain higher accuracy of classification.