Classification Analysis of Topographical Features Using Artificial Neural Network. (2016)

• Record Type: Journal Article
• Authors: Krishnan, R.; Dharani, Andhe
• Is Part Of: Procedia technology. Volume 25(2016)
• Page Start: 399
• Page End: 404
• Publication Date: 2016
• Subjects: crater detection -- backpropagation algorithm -- artificial neural network; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.124
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Identification and classification of the topographical features is a challenging topic in the field of image pattern recognition. Improvement is required in the existing crater detection algorithms because of the pattern types and complexity. Currently more than 500 images are transmitted to earth with a resolution of 5 to 100 meters. The artificial neural network plays an important role in training and classification of image patterns. This paper deals with analysis of crater detection with back propagation algorithm with training and classification, and analysis of execution time for classification of craters.