Using Local Binary Pattern Variance for Land Classification and Crop Identification

The conventional method of compiling statistics on Crop Estimation is done by the Government Ministry to make ground surveys, during which selected farmers or village officials are interviewed regarding their crops. By comparison with the results of previous years, this information is then extrapolated to generate data and predictions on a regional basis. This means that these traditional surveys are both time-consuming and expensive that’s why farmer’s eye estimates are remarkably close to actual crop production figures. Remote sensing has been increasingly identified as an objective, standardized, possibly cheaper and faster methodology for crop production surreys than conventional field investigation. The aim of this research is to evaluate crop discrimination using satellite image data by following remote sensing approach. This research illustrates the use of Local Binary Pattern Variance on satellite images to classify the land in to crop land and non-crop land and to classify different crops. The input image is first enhanced then Local Binary Pattern Variance is used to extract features from the crop images specifically extracting green colors. After identifying the LBPV pattern of each pixel (i,j) in the given image the whole texture image is represented by building a histogram showing intensity values for uniform and non-uniform patterns. 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.