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

• Published in: Journal of agricultural engineering (Pisa, Italy) Vol. 42; no. 4; pp. 9 - 16
• Main Authors: Claudia Arcidiacono, Simona M.C. Porto
• Format: Journal Article
• Language: English
• Published: PAGEPress Publications 01.06.2012
• Subjects: greenhouse, GIS, texture, accuracy assessment
• ISSN: 1974-7071; 2239-6268
• DOI: 10.4081/jae.2011.4.9

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.