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:: COMPARISOM OF WAVELET-BASED AND HHT-BASED FEATURE EXTRACTION METHODS FOR HYPERSPECTRAL IMAGE CLASSIFICATION X.-M. Huang and P.-H. Hsu

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 COMPARISOM OF WAVELET-BASED AND HHT-BASED FEATURE EXTRACTION METHODS FOR HYPERSPECTRAL IMAGE CLASSIFICATION X.-M. Huang * and P.-H. Hsu * * Dept. of Civil Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Rd., Taipei City 10617, Taiwan — (r98521116, hsuph)@ntu.edu.tw Commission VII, WG VII/3 KEY WORDS: Hyperspectral Image, Feature Extraction, Classification, Wavelet Transform, Hilbert-Huang Transform ABSTRACT: Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as “curse of dimensionality.” The commonly used method to solve this problem is dimensionality reduction, and feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with wavelet- based feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods. 1. INTRODUCTION Imaging spectrometer, a technology which was developed in 1980's, can obtain hundreds of spectral bands simultaneously (Goetz et al, 1985). The images acquired with spectrometers are called as hyperspectral images. These images not only re- veal two-dimensional spatial information but also contain rich and fine spectral information. With these characteristics, they can be used to identify surface objects and improve land use/cover classification accuracies. In past three decades, hy- perspectral images have been widely used in different fields such as mineral identification, vegetation mapping, and disaster investigation (Goetz et al., 1985). Because hyperspectral data have the property of high dimen- sionality, image processing methods which have been effective- ly applied to multispectral data in the past are not as proper as to hyperspectral data. For instance, it is ineffective when the traditional statistical classification methods are applied to hy- perspectral images with limited training samples. In other words, the dimensionality increases with the number of bands, the number of training samples for classification should be in- creased as well (Hsu, 2007). This has been termed the “curse of dimensionality” by Bellman (1961). The commonly used meth- od to solve “curse of dimensionality” is dimensionality reduc- tion, which can be divided into two types: feature selection and feature extraction. For hyperspectral images, feature extraction is used to reduce the dimensionality more frequently (Hsu, 2003). Corresponding author. There are two types of feature extraction methods. The first type is based on the statistical property of data. For instance, princi- pal components transform (PCT) is the most commonly used and simple method. Although it concerns the distribution of whole data, some useful features for hyperspectral data will be neglected easily. Discriminant analysis feature extraction (DAFE) is to maximize the between-class scatter and minimize the within-class scatter. Moreover, decision boundary feature extraction (DBFE), which was proposed by Lee and Landgrebe (1993), could find useful features by decision boundaries be- tween different classes. Although DAFE and DBFE are effec- tive and practical algorithms, there are some disadvantages. For example, the maximum number of feature in DAFE is the num- ber of class minus one. Besides, in order to get reliable parame- ters in DAFE or to compute the decision boundaries in DBFE, it still needs adequate training samples (Fukunaga, 1990; Lee and Landgrebe, 1993). The other type of feature extraction methods is based on time- frequency analysis. For example, it has been proven that wave- let-based feature extraction provide an appropriate and effective tool for spectral feature extraction (Hsu, 2003). However, this method has some disadvantages; for instance, it has to select the wavelet basis function in advance, or it is not suitable for non- linear data analysis. Hilbert-Huang transform (HHT) is a rela- tively new adaptive time-frequency analysis tool. It combines empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), and has been used extensively in nonlinear and nonstationary data analysis. In this study, the wavelet transform and HHT are implemented on the hyperspectral data for physi- cally spectral analysis. The spectral features are then extracted

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