C4.5: programs for machine learning
C4.5: programs for machine learning
Data filtering for automatic classification of rocks from reflectance spectra
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
| Hi-index | 0.00 |
The use of remotely sensed data to map aspects of the landscape is both efficient and cost effective. In geographically large and sparsely populated countries such as Australia these approaches are attracting interest as an aid in the identification of areas affected by environmental problems such as dryland salinity. This paper investigates the feasibility of using visible and near infra-red spectra to distinguish between salt tolerant and salt sensitive vegetation species in order to identify saline areas in Southern Victoria, Australia. A series of classification models were built using a variety of data mining techniques and these together with a discriminant analysis suggested that excellent generalisation results could be achieved on a laboratory collected spectra data base. The results form a basis for continuing work on the development of methods to distinguish between vegetation species based on remotely sensed rather than laboratory based measurements.