Automatic Oil Palm Detection and Identification from Multi-scale Clustering and Normalized Cross Correlation

Oil palm cultivation is one of the most important occupation in South East Asia. Since oil palm plantations cover the wide range of area, it is difficult to count the population of oil palm manually. This paper presents a new method to detect and identify oil palms over the plantation from aerial images regard- less of their sizes using features such as shape, size, and texture. The proposed method can handle the problem of identifying oil palms from an aerial image when oil palms are too close to each other which cause them to be detected as a single stand. The process consists of removing non-tree components from an image, distinguishing oil palms from other components, identifying individual oil palm, and counting the number of oil palms. In this paper, oil palm can be detected and distinguished from other components using ideal low-pass filter and normalized cross correlation. Then a proposed multi-scale clustering meth- od and erosion are used to identify individual oil palm from a bush. The method was evaluated on a set of 21 aerial images taken over oil palm plantations from different regions of Thailand by attaching a digital camera to a remote airplane. The experimental results reveal that our proposed method can detect most of the oil palms with the accuracy as high as 90%.