The utility of texture analysis to improve per‐pixel classification for high to very high spatial resolution imagery

By A. Puissant, J. Hirsch, C. Weber. 2005

333 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/1642796b78d1dd12b417c7ea349f08bb77515314

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, background): “literature: the main being textural [3], [4], [18]–[20], morpho-”
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): “Traditionally, texture methods have been evaluated over windows of a single size, the latter being commonly defined on an experimental basis, e.g. in Puissant et al. (2005).”
A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure (2008, background): “The general interest in the exploitation of the image textural information with regard to the discrimination of built-up structures was already demonstrated by [4] and [5] and recent contributions in [6] and [7] merging in a similar way radiometric and textural information for improving the urban cl”
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 ).”
A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification (2009, background): “In most cases, texture increased the per-pixel classification accuracy, especially in urban areas where the images are more heterogeneous ( Puissant et al., 2005 ).”
Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses (2008, methodology): “Puissant et al. (2005) compared the effect of adding a texture band (HOM, DIS, ENT and ASM) to the multispectral bands (R, G, B), and the ws used to calculate the texture parameter on the classification results of various images with different resolutions (1, 2.5, 5 and 10 m).”
SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images (2012, methodology): “In [45], an experimental plan is designed involving four GLCM features and six window sizes.”