A Comparative Study on Satellite Image Classification Using Various Deep Learning Techniques
Abstract
The satellite image classification procedure comprises categorizing
the image pixel values. There are several approaches and procedures for classifying
satellite images. Satellite image categorization algorithms are roughly divided into
three categories: 1) automatic 2) manual and 3) hybrid. Satellite image classification
requires the selection of an appropriate classification method depending on the
criteria. This paper focuses on satellite image categorization methods and
methodologies.
References
Muhammad, S., Aziz, G., Aneela, N. and Muhammad, S. 2012. “Classification
by Object Recognition in SatelliteImages by using Data Mining”. In Proc.
Proceedings of the World Congress on Engineering (WCE 2012), Vol I, July 4 - 6,
London, U.K.
Chaichoke, V., Supawee, P., Tanasak, V. and Andrew, K, S. 2011. "A
Normalized Difference Vegetation Index (NDVI) Time-Series of Idle Agriculture
Lands: A Preliminary Study", Engineering Journal. Vol. 15, Issue 1, pp. 9-16.
Zheng, X., Sun, X., Fu, K. and Hongqi Wang, 2013. “Automatic Annotation of
Satellite Images via Multifeature Joint Sparse Coding With Spatial Relation
Constraint”, IEEE Geoscience and Remote Sensing Letters, VOL. 10, NO. 4, JULY
, pp.652-656.
Anders Karlsson, 2003. “Classification of high resolution satellite images”,
August 2003, available at http://infoscience.epfl.ch/record/63248/files/TPD_Karlss
on.pdf.
Amanda Briney, 2014. “An Overview of Remote Sensing”, May 16, 2014.
[online] available at http://geography.about.com/od/geographictechnology/a/r
emotesensing.ht
