POST-CLASSIFICATION APPROACH BASED ON GEOSTATISTICS TO REMOTE SENSING IMAGES : SPECTRAL AND SPATIAL INFORMATION FUSION
Classification of remote sensing imagery provides an inexpensive yet efficient approach to land cover mapping. In supervised image classification, training samples are collected through certain sampling schemes, which are used to derive classification rules, aiming for adequate accuracy for the ap...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2012-08-01
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Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXIX-B7/247/2012/isprsarchives-XXXIX-B7-247-2012.pdf |
Summary: | Classification of remote sensing imagery provides an inexpensive yet efficient approach to land cover mapping. In supervised image
classification, training samples are collected through certain sampling schemes, which are used to derive classification rules, aiming
for adequate accuracy for the applications at hand. However, in conventional classification methods, the potential of training
samples in terms of locational information is not tapped further, confounding the classification accuracy to the limited separability
inherent to the given input feature vector. This paper explores two methods pertaining to geostatistics, i.e., simple kriging with local
mean and cokriging, to predict class occurrences based on training samples' indicator transforms (location and classes) and
spectrally derived class probabilities, thus calibrating the a posterior class probability vectors derived from initial spectral
classification. The results showed that classification accuracy is significantly increased by these two methods for utilizing spatial
information contained in training samples and initial spectral classification, compared with those obtainable with spectral
classification. Moreover, the proposed methods constitute a valuable strategy for making fuller use of information residing in
training data for improving spectrally derived classification, which is independent of the specific classifiers initially adopted for
image classification. |
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ISSN: | 1682-1750 2194-9034 |