Adaptive spatial sampling schemes for the detection of minefields in hyperspectral imagery

Thomas, Alan M.; Cathcart, J. Michael

doi:10.1117/12.784965

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…This approach is usually used when training data for supervised classification are not obtainable, or are too expensive to acquire, or when the dataset presents high dimensions. Specifically, literature presents several methods to achieve a correct unsupervised classification for detecting vegetation; among these Maximum Likelihood (Gromyko and Shevlakov, 2004), K-means (Thomas and Cathcart, 2008) and Self Organizing Map (Yuan et al, 2009) are some of the techniques more used in the last years. An alternative to the unsupervised classification methods for the automatic detection of vegetation, is the calculation of a conventional spectral vegetation index: the most commonly used index is the Normalized Difference Vegetation Index (NDVI) (Saha et al, 2005, Xie et al,2008.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Unsupervised Vegetation Classification Methods From VHR Images After Shadows Removal by Innovative Algorithms

Movia

Beinat

Crosilla

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the classification of the image components and for the extraction of the needed information. The aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental biochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been experimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned by the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of them are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based on RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based on Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called "not-centered oblique Procrustes" and "anisotropic Procrustes" methods to efficiently restore brightness with respect to a linear correlation correction based on the Cholesky decomposition. Some experimental results obtained by different classification methods after shadows removal carried out with the innovative algorithms are presented and discussed.

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