Performance of Combination of Texture and Object Based Techniques in Image Classification for Urban Land Cover

Abstract

Satellite imagery paves way to obtain tangible information through remote sensing techniques.  It is necessary to classify the image in order to extract the features.  There exist various classification techniques and algorithms to retrieve various features from imagery.  As the technology development proceeds in a faster track it is necessary to compensate its advancements by developing new techniques for feature retrieval.  As far as high resolution satellite imagery are concerned object based feature retrieval and texture based feature retrieval techniques are gaining its importance.  The texture based feature retrieval has various techniques involved in it, among which Haralick’s texture parameters has much importance.  Thereby object based technique also has its own way of algorithms and processes for feature retrieval.  The eCognition software provides a platform for combining texture and object based technique.  It is well known from various journals that object based technique is best for classifying high resolution imagery.  Thus the image is primarily segmented into objects for classification.  The Haralick’s texture parameters which serve well in classification of urban land cover is chosen by computing statistical analysis.  Finally the chosen texture parameter is adopted in the classification of the objects.  The classified imagery is checked for accuracy and a high accuracy of 94.5% is obtained.