Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery
New high-resolution sensors offer the potential to apply remote sensing to a new, finer scale of problems. However, the highest spatial resolution imagery typically has only a single spectral band, and therefore, classification based on spatial patterns is required. One application
particularly suited to spatial classification is the identification of orchards and vineyards. Orchards and vineyards have a distinctive repeating pattern that can be identified by an analysis of local autocorrelation patterns. A classification algorithm based on an analysis of autocorrelograms
was developed, and tested using Ikonos panchromatic imagery of Granger, Washington. The spatial autocorrelation-based classification resulted in an estimated accuracy of 0.954 and κ of 0.900. Errors of omission for orchards and vineyards were slightly higher than errors of commission
(11–14 percent versus 3–6 percent). By comparison, a maximum likelihood classification of 32 gray level co-occurrence texture bands had a lower accuracy (0.865), with a κ of 0.701, and errors of omission and commission as high as 35 percent and 57 percent, respectively.
Document Type: Research Article
Publication date: 01 February 2005
- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies. - Editorial Board
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