An Improved RF-PNN Based Annotation Model With Texture and Edge Based Feature Descriptors

Abstract

High informative feature descriptors always improves the classification process. In order to classify the earth surface, it is essential to annotate satellite images using itshigh informative feature descriptors. In this proposed work, an annotation framework has been implemented to improve the image discrimination by extracting texture and edge based feature vectors. So the combination of these features subsequently fed into the Random Forest based Probability Neural Network (RF-PNN) classifier to make an annotation model. The experimental analysis with comparisons shows that the proposed annotation model well performed with earlier works and comparative results of benchmark datasets of AID dataset, UC-Merced Land-Use dataset and WHU-RS19 datasets have been documented with analysis.