Robust Regressive Feature Extraction Based Relevance Vector Margin Boosting For Aerial Images Scene Classification

Dr.K. Murugan*, Assistant Professor, Department of Computer Science, Government College for Women, Kolar, India.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 614-621 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7636049420/2020©BEIESP | DOI: 10.35940/ijeat.D7636.049420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Aerial image scene classification is a key problem to be resolved in image processing. Many research works have been designed for carried outing scene classification. But, accuracy of existing scene classification was lower. In order to overcome such limitation, a Robust Regressive Feature Extraction Based Relevance Vector Margin Boosting Scene Classification (RRFERVMBSC) Technique is proposed. The RRFE-RVMBSC technique is designed for improving the classification performance of aerial images with minimal time. The RRFERVMBSC technique comprises two main processes namely feature extraction and classification. Initially, RRFE-RVMBSC technique gets number of aerial images as input. After taking input, Robust Regressive Independent Component Analysis Based Feature Extraction process is performed in order to extract the features i.e. shape, color, texture and size from aerial image. After completing feature extraction process, RRFERVMBSC technique carried outs Ensembled Relevance Vector Margin Boosting Classification (ERVMBC) where all the input aerial images are classified into multiple classes with higher accuracy. The RRFE-RVMBSC technique constructs a strong classifier by reducing the training error of weak RVM classifier for effectual aerial images scene categorization. The RRFERVMBSC technique accomplishes simulation work using parameters such as feature extraction time classification accuracy and false positive rate with respect to number of aerial images.
Keywords: Aerial image, Relevance Vector Margin Boosting, Robust Regressive Independent Component Analysis, Strong classifier, Training Error, Weak RVM