Description

ABSTRACT:

                We propose a robust  natural scene and Geographic   retrieval using a supervised classifier which concentrates on extracted features. Gray level co-occurance matrix and invariant Features are  implemented to extract the features from images. SVM classification is performed on the dataset and it is classified into two categories such as Geographic or natural images. The query image is classified by the classifier to a particular class and the relevant images are retrieved from the database using Euclidean distance. This paper investigates local invariant features for geographic (overhead) image retrieval. Local features are particularly

well suited for the newer generations of aerial and satellite imagery whose increased spatial resolution, often just tens of centimeters per pixel, allows a greater range of objects and spatial patterns to be recognized than ever before. Local invariant features have been successfully applied to a broad range of computer vision problems and, as such, are receiving increased attention from the remote sensing community particularly for challenging tasks such as detection and classification.