Texture feature extraction for classification of remote sensing data using wavelet decomposition: A comparative study

The extraction of texture features from high resolution remote sensing imagery provides a complementary source of data for those applications in which the spectral information is not sufficient for identification or classification of spectrally heterogeneous landscape units. However, there is a wide range of texture analysis techniques that are used with different criteria for feature extraction: statistical methods (grey level coocurrence matrix, semivariogram analysis); filter techniques (energy filters, Gabor filters); or the most recent techniques based on wavelet decomposition. The combination of parameters that optimize a method for a specific application should be decided when these techniques are used. These parameters include the neighbourhood size, the distance between pixels, the type of filter or mother wavelet used, the frequency or the standard deviation used to create the Gabor filters, etc. The combination of parameters and the texture method used is expected to be key in the success and efficiency of these techniques for a particular application. In this study, we analyze several texture methods applied to the classification of remote sensing images with different types of landscapes, as well as the optimal combination of parameters for each group of data. For this purpose, we created a database with high resolution satellite and aerial images from two types of environments, representing two of the main applications of texture analysis in remote sensing: Urban and forestry. The texture classes defined in urban applications involve heterogeneity and symmetry, while in forest applications is important to know the type and density of vegetation. The results show that the type of application determines the technique and the combination of parameters to be used for optimizing accuracy. The combination o