IMPROVING PER-PIXEL CLASSIFICATION OF CROP-SHELTER COVERAGE BY TEXTURE ANALYSES OF HIGH-RESOLUTION SATELLITE PANCHROMATIC IMAGES

Submitted: 22 June 2012
Accepted: 22 June 2012
Published: 22 June 2012
Abstract Views: 960
PDF: 712
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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.

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How to Cite

Arcidiacono, C. and Porto, S. M. (2012) “IMPROVING PER-PIXEL CLASSIFICATION OF CROP-SHELTER COVERAGE BY TEXTURE ANALYSES OF HIGH-RESOLUTION SATELLITE PANCHROMATIC IMAGES”, Journal of Agricultural Engineering, 42(4), pp. 9–16. doi: 10.4081/jae.2011.4.9.

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