Proceedings Article | 9 December 2015
KEYWORDS: Earth observing sensors, Image classification, Roads, Remote sensing, Landsat, Pattern recognition, Defense technologies, Evolutionary algorithms, Artificial neural networks, Information technology
High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing
applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source,
Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been
tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial
Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26%
and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral
Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%,
respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is
much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM,
SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively.
Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land
and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their
advantages and disadvantages.
These results show that, under the same conditions, the same images with different classification methods to classify, there
will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification
accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.
