Classification of Hyperspectral Image using Convolutional Neural Network to Detect Coastal Water Features

Title
Classification of Hyperspectral Image using Convolutional Neural Network to Detect Coastal Water Features
Author(s)
김태호; 양찬수
KIOST Author(s)
Kim, Tae Ho(김태호)Yang, Chan Su(양찬수)
Publication Year
2018-11-08
Abstract
In this study, classification method of hyperspectral images using convolutional neural network (CNN) is introduced. In general, two-dimensional image data are used as the inputs of CNN classification model. Therefore, 2-D input data for each of the image pixels under specific area were prepared by using the reflectance values in all spectral bands. Thus, two-dimensional grey scale image of 1296 pixels was generated for each pixel under the region of interest (ROI) area obtained from PIKA-II hyperspectral camera. A CNN network was constructed to distinguish human, sea, sand, ship and rock in the image. The CNN model training based classification for each target was performed which resulted in highest classification accuracy for the sand (86.4%).ch of the image pixels under specific area were prepared by using the reflectance values in all spectral bands. Thus, two-dimensional grey scale image of 1296 pixels was generated for each pixel under the region of interest (ROI) area obtained from PIKA-II hyperspectral camera. A CNN network was constructed to distinguish human, sea, sand, ship and rock in the image. The CNN model training based classification for each target was performed which resulted in highest classification accuracy for the sand (86.4%).
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22885
Bibliographic Citation
International Conference on Space, Aeronautical and Navigational Electronics 2014, pp.153 - 156, 2018
Publisher
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS
Type
Conference
Language
English
Publisher
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS
Related Researcher
Research Interests

Satellite Oceanography,Marine Safety & Security,Remote Sensing,위성해양학,해양 안전 및 보안,원격탐사

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