XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/3: INFORMATION EXTRACTION FROM HYPERSPECTRAL DATA]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ASSESSING THE SIGNIFICANCE OF HYPERION SPECTRAL BANDS IN FOREST CLASSIFICATION G. J. Newnham, D. Lazaridis, N. C. Sims, A. P. Robinson, D. S. Culvenor

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia ASSESSING THE SIGNIFICANCE OF HYPERION SPECTRAL BANDS IN FOREST CLASSIFICATION G. J. Newnham®, D. Lazaridis?, N. C. Sims?, A. P. Robinson?, D. S. Culvenor? a CSIRO Division of Land and Water and Sustainable Agriculture Flagship, Clayton South, Victoria, Australia Department of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia KEYWORDS: Forest, Classification, Hyperspectral, Ensemble, Decision Tree, Random Forests ABSTRACT: The classification of vegetation in hyperspectral image scenes presents some challenges due to high band autocorrelations and problems dealing with many predictor variables. The Random Forests classification method is based on an ensemble of decision trees and attempts to address these issues by dealing with only a subset of image bands in each node of each decision tree. Random Forests has previously been used for classification of vegetation using hyperspectral data. However, the variable importance measure that is a by-product of the technique has largely been ignored. In this study we investigate the spectral qualities of variable importance in the classification of forest and non-forest in a single Hyperion scene. The spectral importance curve showed broad bands of importance over wavelength regions known to be significant in biochemical absorption. 1. INTRODUCTION Certain biological and statistical challenges can inhibit the successful use of hyperspectral data for mapping forest extent. Absorption by plant materials in vivo generally occur as broad wavelength bands leading to auto-correlation in vegetation reflectance spectra. In addition, many statistical modelling methods have a tendency to over-fit to noise in cases with many predictor variables (Bajcsy and Groves, 2004). Consequently, classification accuracy may be highest when only a small a subset of predictor variables is used (Hughes, 1968). The ensemble decision tree approach described as Random Forests (Breiman, 2001) is suited to addressing these challenges and has been shown to be superior to linear, quadratic and penalised discriminant analysis when using hyperspectral satellite data (Everingham et al., 2007; Sluiter and Pebesma, 2010). Random Forests models also generate a measure of variable importance. High variable importance has been used for selecting narrow bands (Chan and Paelinckx, 2008) and spectral indices (Ismail and Mutanga, 2010) for inclusion in refined classification models. However, the spectral characteristics of variable importance have not been fully explored. We consider variable importance for a classification of forests and non-forests based on a Hyperion image over high value forest site in Tasmania. Spectral characteristics of the importance curve are compared to known absorption and reflectance characteristics of leaf biochemicals. 2. METHODS The Hyperion scene used in this study was captured on the p^ of March 2010 over the Warra Long Term Ecological Research (LTER) site in southern Tasmania (Brown et al., 2001). The image was 88km in the along track direction and included mainly forested land in the south, while grassland and pasture dominated in the north. Pre-processing was performed using the methods described by (Datt et al., 2003) and then registered to a orthocorrected mosaic of Landsat Thematic Mapper images produced as part of the Australian National Carbon Accounting System (Furby, 2002). A Tasmanian Government state-wide vegetation map was used for training and validation of the classification models. The map is based on aerial photo interpretation and field validation, and includes 154 classes as described by Harris and Kitchener (2005). These classes were aggregated into generic forest and non-forest classes and a raster map created on the same grid as the Hyperion image. First, we applied the implementation of Random Forests by Liaw and Wiener (2002) to discriminate forest from non-forest classes in the Hyperion image. For each class, 10000 pixels were selected at random as the training set. In each model run, 1000 decision trees were generated. Classification accuracy was assessed across the entire Hyperion scene. The wavelength regions that best discriminate forest from non-forest classes were inferred from the variable importance spectrum. These wavelengths were then compared to published biochemical absorption features to examine which parameters of forest biochemistry may be contributing to the spectral separation of forested from non-forested areas. 3. RESULTS The classifications of the Hyperion image were assessed in terms of overall accuracy and the Kappa statistic (Cohen, 1960). These are summarised in Table 1. Training accuracy was comparable to other published results. Interestingly, when the model was applied to all pixels in the Hyperion scene, the overall accuracy was maintained and the kappa statistic increased slightly. This is not a large increase, but does indicate the stability of the model when applied outside the original data on which it was built. The significance of Hyperion spectral bands in discriminating the forest and non-forest classes were assessed using the measure of variable importance produced using the Random Forests method. The plot of variable importance as a function of wavelength showed strong auto-correlation, with dominant peaks in significant biochemical absorption regions.

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