A multiscale texture analysis procedure for improved forest stand classification

By C. Coburn, A. Roberts. 2004

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/bbdb11e789c1cf426bfdfa97d7c6a8d9a9b8955f

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Citations in the corpus

Analysis of co‐occurrence and discrete wavelet transform textures for differentiation of forest and non‐forest vegetation in very‐high‐resolution optical‐sensor imagery (2008, background): “As reported in Coburn and Roberts ( 2004 ), the standard approach to texture analysis is to process a single band of spectral information with a fixed processing window.”
Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery (2009, methodology): “In general, classification accuracies are improved by the use of texture [7]–[10].”
Nonparametric estimation of stem volume using airborne laser scanning, aerial photography, and stand-register data (2006, methodology): “These textural features have been widely used in remote sensing during the last decades (e.g., Haralick et al. 1973; Weszka et al. 1976; Marceau et al. 1990; Peddle and Franklin 1991; Muinonen et al. 2001; Coburn and Roberts 2004; Tuominen and Pekkarinen 2005).”
High-resolution image texture as a predictor of bird species richness (2006, background): “The usefulness of first- and second-order statistics in the detection of structural patterns from satellite imagery has led to their application in image classification and segmentation (Franklin et al., 2000; Coburn & Roberts, 2004; Puissant et al., 2005).”
Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues (2009, methodology): “The grey-level co-occurrence matrix (GLCM) (Haralick et al., 1973; Haralick and Shanmugam, 1974) is one of the most common algorithms for computing texture measures (Coburn and Roberts, 2004; Franklin et al., 2000, 2001a, and 2001b; Zhang et al., 2004).”
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research (2016, unknown): “Incorporationof texture is a popular technique for classi ﬁ cation improvement (Coburn & Roberts, 2004; Ji, 2000; Shackelford & Davis, 2003).”
Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data (2009, methodology): “Semivariance and other texture measures applied to remote sensing data have been used extensively to identify unique forest structure (Woodcock & Strahler, 1987; Woodcock et al. (1988), Cohen et al., 1990; Franklin et al., 2001; Song & Woodcock, 2002; Coburn & Roberts, 2004).”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, methodology): “Moreover, the mean height and mean DBH with 8% and 13.7% error of estimation, respectively, were estimated more accurately than basal area, stand volume, and stocking, where the error of estimation is up to 30%.”
A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “As a result, MRFs have been applied to solve many remote sensing image analysis problems, including classification, change detection, sub-pixel analysis, and segmentation [Shekarforoush et al., 1996; Jia and Richards, 2008; Fauvel et al., 2013].”
Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales (2017, background): “Several window sizes may be included for detailed textural analysis where this is desirable (Coburn and Roberts 2004).”