Date of Award


Degree Type


Degree Name

Master of Science in Natural Resources


Natural Resources Science

First Advisor

Yeqiao Wang


Satellite remote sensing data have been broadly applied in extraction of thematic land cover information. The multi-spectral classification is the mostly used method. Traditional supervised classification algorithms are in the category of statistical pattern recognition. Due to the limitations of spatial and spectral resolutions of remotely sensed data, traditional classification has always been challenged in thematic accuracy of land cover mapping. To improve the performance, new technical approaches are in demand.

Artificial Neural Network is one of the emerging approaches in multi-spectral remote sensing and multi-source spatial data handling. However, none-optimized method always met difficulties to get an ideal result. In this study, Backpropagation ANN (BPANN) was applied for the classification of 1999 Landsat- 7 Enhanced Thematic Mapper Plus (ETM+) data. Several optimization algorithms including Conjugate Gradient, Resilient back-propagation (RPROP), and Gray-code coding method were also applied to improve the performance of the classification. The result shows that the optimized BP ANN is a more robust and advanced approach in remote sensing image analysis than the traditional statistical based or none-optimized BP ANN based classification method. Therefore, application of satellite derived remote sensing data in natural resource and environmental mapping can be improved.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.