Supervised Classification of Remote Sensed Data using Support Vector Machine

Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function In this paper the support vector machine classification method is applied to remote sensed data Two different formats of remote sensed data is considered for the same The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region The second format used is raster format which contains image of Andhra Pradesh state in India Support vector machine classification method is used herein to classify the raster image into categories One category represents land and the other water wherein green color is used to represent land and light blue color is used to represent water Later the classifier is evaluated using kappa statistics and accuracy parameter

Image Information Retrieval based on Edge Responses, Shape and Texture Features using Datamining Techniques

The present paper proposes a new technique that extracts significant structural, texture and local edge features from images. The local features are extracted by a steady local edge response that can sustain the presence of noise, illumination changes. The local edge response image is converted in to a ternary pattern image based on a local threshold. The structural features are derived by extracting shapes in the form of textons. The texture features are derived by constructing grey level co-occurrence matrix (GLCM) on the derived texton image. A new variant of K-means clustering scheme is proposed for clustering of images. The proposed method is compared with various methods of image retrieval based on data mining techniques. The experimental results on Wang dataset shows the efficacy of the proposed method over the other methods

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes