18 October 2016 Tree detection in orchards from VHR satellite images using scale-space theory
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Abstract
This study focused on extracting reliable and detailed information from very High Resolution (VHR) satellite images for the detection of individual trees in orchards. The images contain detailed information on spectral and geometrical properties of trees. Their scale level, however, is insufficient for spectral properties of individual trees, because adjacent tree canopies interlock. We modeled trees using a bell shaped spectral profile. Identifying the brightest peak was challenging due to sun illumination effects caused 1 by differences in positions of the sun and the satellite sensor. Crown boundary detection was solved by using the NDVI from the same image. We used Gaussian scale-space methods that search for extrema in the scale-space domain. The procedures were tested on two orchards with different tree types, tree sizes and tree observation patterns in Iran. Validation was done using reference data derived from an UltraCam digital aerial photo. Local extrema of the determinant of the Hessian corresponded well to the geographical coordinates and the size of individual trees. False detections arising from a slight asymmetry of trees were distinguished from multiple detections of the same tree with different extents. Uncertainty assessment was carried out on the presence and spatial extents of individual trees. The study demonstrated how the suggested approach can be used for image segmentation for orchards with different types of trees. We concluded that Gaussian scale-space theory can be applied to extract information from VHR satellite images for individual tree detection. This may lead to improved decision making for irrigation and crop water requirement purposes in future studies.
Conference Presentation
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Milad Mahour, Valentyn Tolpekin, Alfred Stein, "Tree detection in orchards from VHR satellite images using scale-space theory", Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040B (18 October 2016); doi: 10.1117/12.2241529; https://doi.org/10.1117/12.2241529
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