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

)- bod IS 2.3 Comparisons between Wavelet Transform and Hilbert- Huang Transform Wavelet transform and Hilbert-Huang transform are both time- frequency analysis tools, so that they can analysis the variation of data in both time and frequency domain. Table 1 shows some differences between wavelet transform and HHT. Firstly, wavelet transform have complete theoretical base and have to define a basis function before using it; whereas, HHT with empirical theoretical base has an adaptive basis, which can analysis data adaptively. Second, wavelet transform computes frequency by convolution operation; while, the frequency is derived by differentiation rather than convolution in HHT. However, wavelet transform and HHT can present the results in time-frequency-energy space. Finally, wavelet transform is suitable for nonstationary data but is unsuitable for nonlinear data. On the contrary, HHT is suitable for both nonlinear and nonstationary data. Therefore, HHT is a superior tool for time- frequency analysis of nonlinear and nonstationary data (Huang, 2005). 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 Wavelet Transform HHT Theoretical Base Theory complete Empirical Basis A prior Adaptive Frequency Convolution Differentiation Pros État Energy-time- Energy-time- frequency frequency Nonlinear Data Unsuitable Suitable Nonstitionay Suitable Suitable Data Table 1. Comparisons between Wavelet and Hilbert-Huang Transform (Huang, 2005) 3. HYPERSPECTRAL IMAGE FEATURE EXTRACTION 3.1 Datasets Description In this study, an AVIRIS data set is used to test the performance of using wavelet transform and HHT on hyperspectral image feature extraction and classification. The AVIRIS data set shown in Figure 1(a) is the well-known Cuprite data set, which is a mineral region at Nevada. The image size of the test field is 350x350. The number of bands is 224. Figure 1(b) also shows a mineral map produced in 1995 by USGS. In this study, we choose 6 classes from this map (Table 2) for feature extraction and classification. Table 2 also shows the number of training samples and check sample for image classification. # of training # of check Class names samples samples Alunite 100 50 Kaolinite 100 50 Muscovite 100 50 Calcite 100 50 Montmorillonite 100 50 Kaolinite-- i ao me Semectite or 100 50 uscovıte Table 2. The 6 chosen classes Cuprite, Nevada AVIRIS 1995 Data | USGS Clark & Swayze Tricorder 3.3 product E K-Alunite 150C K-Alunite 250C K-Alunite 450C Na82-Alunite 100C Na40-Alunite 400C Kaolinite wx1 2 Kaolinite px! Kaolinite+smectite cr muscovite Montmorillonite alcite +Kaolinite a * Montmorillonite ~ low-Al muscovite ed-Al muscovite gh-Al muscovite arosite uddingtonite =A Chalcedony Nontronite Pyrophyllite + alunite Chlorite + lontmorillonite or Muscovite Chlorite (b) Mineral map in Cuprite(USGS Spectroscopy Lab, 1998) Figure 1. An AVIRIS data set of Cuprite 3.2 Wavelet-Based Feature Extraction The orthogonal wavelet transform can decompose a signal into the low-frequency components that represent the optimal approximation, and the high-frequency components that represent detailed information of the original signal (Mallat, 1989). The decomposition coefficients in a wavelet orthogonal basis can be computed with a fast algorithm that cascades discrete convolutions with conjugate mirror filters (CMF) h and g, and subsamples the outputs. The decomposition equations are described as following: a, [pl=) hin-2pla [n] gn (10) j*l d,,[p]= S gIn- 2p]a [n] n=-0 a; is the approximation coefficients at scale 2, and aj, and dj.; are respectively the approximation and detail components at

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