A Framework of Hyperspectral Image Compression using Neural Networks
Christian Martínez Nieves
Rafael Rivera Soto
Carlos Velez
Jenipher Gonzalez
Abstract:
Keywords—Image Compression, ANN, HSI
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A Framework of Hyperspectral Image Compression using Neural Networks | ||
| Published in: | Proceedings of the 13th Latin American and Caribbean Conference for Engineering and Technology: Engineering Education Facing the Grand Challenges, What Are We Doing? | |
| Date of Conference: | July 29 - 31, 2015 | |
| Location of Conference: | Santo Domingo, Dominican Republic | |
| Authors: | Yahya M. Masalmah Christian Martínez Nieves Rafael Rivera Soto Carlos Velez Jenipher Gonzalez | |
| Refereed Paper: | #189 | |
Abstract: | ||
| Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows us to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. This paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.
Keywords—Image Compression, ANN, HSI | ||