Image Classification based on Feature Extraction with AlexNet Architecture - Cho, Zarli; Kyi, Khin Myo; Oo, Kyi Thar (International Journal of Computer Sciences and Engineering, 2020)
Deep learning has emerged as a new area in machine learning and is applied to a number of signal and image applications. Although the existing traditional image classification methods have been widely applied in practical problems, such as unsatisfactory effects and weak adaptive ability. The main purpose of the work presented in this paper, is to apply the concept of image feature extraction with AlexNet Convolutional Neural Networks (CNN) in Digital Elevation Map and Topological Map boundary classification of Yangon City in Myanmar. The automated derivation of topographic data from DEMs is faster, less subjective and provides more reproducible measurements than traditional manual techniques applied to topographic maps. Data are acquired from the United States Geological Survey (USGS) database. This study is supposed to handle of geospatial information and production of maps. Geospatial users have to understand the distortion characteristics of each maps. The analysis of this result is revealed that has a good classification accuracy for all the tested maps based on the proposed system.