Crop Identification Using Gabor Wavelet Transform

This paper illustrates the use of Gabor wavelet transform on satellite images to classify the land in to crop land and noncrop land and to classify different crops. The input image is enhanced first using Colour Space Transform and Discrete Cosine Transform, and then a filter bank consisting of Gabor wavelets is used to extract texture features from the satellite image. The feature formation process models the texture features from Gabor filter bank as a Gaussian distribution. The use of Gabor filters is driven by the potential they have to isolate texture according to particular frequencies and orientations. The parameters that define a Gabor filter are its frequency, standard deviation and orientation. By varying these parameters, a filter bank is obtained that covers the frequency domain almost completely. A texture image database of different crops is created. The texture features of the input image are then compared with texture features obtained from the image database of different crops and the different types of crops are identified.