@article{Arcidiacono_Porto_2012, title={IMPROVING PER-PIXEL CLASSIFICATION OF CROP-SHELTER COVERAGE BY TEXTURE ANALYSES OF HIGH-RESOLUTION SATELLITE PANCHROMATIC IMAGES}, volume={42}, url={https://www.agroengineering.org/jae/article/view/jae.2011.4.9}, DOI={10.4081/jae.2011.4.9}, abstractNote={Actual research challenges in automated recognition of crop shelters regard, among other issues, the accuracy of classification, contour detection and typology identification. In this field the use of high-resolution multispectral images has been found to improve the feature recognition in comparison to RGB images or low resolution multispectral ones. As for classification methodologies, per-pixel and object-oriented ones offer different tools to cope with image recognition and feature extraction. In this study, to improve the classification of cropshelter coverage, the per-pixel method was applied to high-resolution multispectral images, coupled with a texture analysis of high-resolution panchromatic images. In detail, the results of the classification accuracy assessment achieved by the use of native high-resolution panchromatic images and RGB-band images resampled accordingly, were compared with those found in a previous study in which panchromatic images degraded to the RGB-band image resolution were used. The results show that the proposed methodology is suitable to improve crop-shelter classification quality and contour detection of parcels.}, number={4}, journal={Journal of Agricultural Engineering}, author={Arcidiacono, Claudia and Porto, Simona M.C.}, year={2012}, month={Jun.}, pages={9–16} }