Paper Info
Title
Automatic rice-crop mapping using maximum likelihood SAR segmentation and Gaussian expectation maximisation
Abstract
Accurate, large scale crop monitoring requires the use of weather-independent sensors such as SAR. Rice is the staple food in many parts of Asia and knowledge of rice growth provides valuable economic and environmental information. Extensive pixel accuracy ground truth from an area of Kojima, Japan is used to compare the accuracy of unsupervised mapping algorithms. An acceptable classification is achieved by Gaussian expectation maximisation applied to existing maximum-likelihood (ML) based segmentation methods. Using a Bayesian extension of the ML segmentation scheme gives a dramatic improvement in accuracy which results in a pixel classification accuracy of 86% (68% kappa) and an essentially exact estimate of rice coverage within a 1000 hectare area.
Year
DOI
Venue
2002
10.1109/IGARSS.2002.1025078
IGARSS
Keywords
Field
DocType
agriculture,geophysical signal processing,geophysical techniques,image classification,image segmentation,radar imaging,remote sensing by radar,synthetic aperture radar,vegetation mapping,ad 2001,bayes method,bayesian extension,gaussian expectation maximisation,japan,kojima,oryza sativa,sar,accuracy,automatic mapping,crops,geophysical measurement technique,ground truth,mapping algorithm,maximum likelihood segmentation,radar remote sensing,rice,bayesian methods,maximum likelihood estimation,maximum likelihood,backscatter,agricultural engineering,ecosystems,layout,radar
Computer vision,Computer science,Segmentation,Synthetic aperture radar,Remote sensing,Image segmentation,Ground truth,Gaussian,Artificial intelligence,Pixel,Contextual image classification,Bayesian probability
Conference
Volume
ISSN
Citations 
1
2153-6996
0
PageRank 
References 
Authors
0.34
3
6
Name
Order
Citations
PageRank
Kazuo Ouchi111215.71
g davidson200.34
Genya Saito3447.04
Naoki Ishitsuka431.19
nobuyuki mohri500.34
Seiho Uratsuka63611.81