Evaluation of an imaging sensor for detecting vegetation using different waveband combinations

This paper uses data collected from an earlier reported imaging sensor to investigate the classification of vegetation from background. The sensor uses three wavebands, red; green; and near infra-red (NIR). A classification method (the alpha method) is introduced which is based on a model of the light source and the reflecting surface. The alpha method is compared with two ratio methods of classification (red/NIR and red/green) and two single waveband methods of classification (NIR and green intensity). The Receiver Operating Characteristic Curve (ROC) is used to evaluate the classifications on realistic test images. ROCs compare the `true positive ratioʹ with `the false positive ratioʹ as the classification parameter varies. The area under the ROC gives a measure of how well an algorithm performs. Measurements on the ROC show that the alpha and ratio methods all perform reasonably well with the red/green ratio giving slightly poorer performance than the alpha method and the red/NIR ratio. The single waveband methods perform significantly less well with green intensity easily the worst. The alpha and ratio methods have `bestʹ thresholds that correspond with detectable histogram features when there is a significant amount of vegetation in the image. The physical basis for the alpha method means that there is a detectable mode in the histogram that corresponds with the `bestʹ threshold even when there is only a small amount of vegetation. The single waveband methods do not produce histograms, which can easily be analysed, and so their use should be confined to simple images.