Semantic Geo-Image Classification Using Image Processing Techniques

Satellite image classification is one of the most significant applications in remote sensing. Remote sensing data obtained from different optical sensors have been commonly used to characterize and quantity land information. However, conventional optical remote sensing is limited by weather conditions. Synthetic aperture radar (SAR), with the allweather and all-time advantages, is important in the domain of earth observation. Polarimetric SAR (PoISAR) images can provide more target information and facilitate improvement of the land cover classification accuracy. Therefore, land cover classification for PolSAR images is important in remote sensing, especially for those areas that change drastically with season. This project implement algorithm for the land classification for the PoISAR images. We can propose multilevel semantic features approach to extract the high level features such as Entropy/Anisotropy/Alpha values. And implement physical scattering properties and implement Latent Dirichlet allocation scheme to discover high level semantics to provide histogram for each pixels. Finally implement KNN classification to classify the PoISAR images with various class labels such as water, land and other properties. Experimental results validate the feasibility of the proposed method for land cover classification of the various places, Le., the overall accuracy reaches up to 90.91 %, while that for the method based on the Wishart distance is 85.01 %, which exhibits the superiority of the proposed method over state of art classification in the various geo spatial data.