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

scale 2” (Mallat, 1999). Because of sub-sampling, the length of the original signal is reduced after applying conjugate mirror filer to the original signal. With these properties, wavelet decomposition is implemented on dimensionality reduction of hyperspectral images (Hsu, 2003). Linear and nonlinear wavelet-based feature extraction (WFE) methods are applied to hyperspectral images in this study. In linear WFE, the approximation coefficients a; are regarded as features for classification. On the other hand, nonlinear WFE consider approximation coefficient a; as well as detail coefficient d; and select M largest wavelet coefficients as important features for classification (Hsu, 2003). In the experiments, the wavelet function used in linear and nonlinear WFE is Daubechies 3 wavelet (Daubechies wavelet with 3 vanishing moments). The experiment procedure of wavelet- based feature extraction is illustrated in Figure 2. 3.3 HHT-Based Feature Extraction According to the characteristics of time-frequency analysis of HHT, the HHT will be applied to spectral curves of each pixel in hyperspectral image. First of all, Hilbert-Huang transform is implemented on a spectral curve. The instantaneous frequency and amplitude of each component will be calculated. Then Hil- bert spectrum is formed by using instantaneous frequency and amplitude. The residual information is also considered in this spectrum. After that, the M largest values in the Hilbert spec- trum are selected as the important features of the spectral curve for classification. These features are sorted by the bands where the feature is located. If more than two features have same loca- tion of bands, sort the features according to their frequency. Fi- nally, the extracted features are used as the inputs for classifica- tion. Maximum likelihood classifier is used in this study. The procedure of feature extraction using Hilbert-Huang transform is illustrated in Figure 2 as well. N-dimensional Hyperspectral Image Feature Extraction Wavelet Transform / Hilbert-Huang Transform ' ; de Feature T ! 1 1 : En Feature Selection : xx Au Training Data Training v Classification (Maximum Likelihood Classification) Figure 2. The flow chart of wavelet-based or HHT-based feature extraction 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 4. EXPERIMENTS There are three experiments in this study. The first experiment is to compare the performance between unsupervised and su- pervised HHT-based feature extraction. The second experiment is to compare different WFE methods. Finally, the HHT-based feature extraction methods are compared with wavelet-based feature extraction methods mentioned in section 3.2 and 3.3. 4.1 Experiment I: Comparison of Classification Results between Unsupervised and Supervised HHT-Based Feature Extraction Methods The purpose of the experiment is to test the performance of un- supervised HHT-based feature extraction method. In addition, the features of supervised HHT-based feature extraction are se- lected by computing Bhattacharyya distances from the features extracted by unsupervised HHT-based methods. The classifica- tion accuracies are calculated for various numbers of features by HHT-based methods. Figure 3 shows the classification accuracies with different HHT-based feature extraction methods. We can find that both unsupervised and supervised HHT-based methods have good classification accuracies. The classification results both conform to Hughes phenomenon that accuracy increases at first and then accuracy decline when the number of features increases with constant number of training samples. Compared with unsupervised HHT-based method, supervised HHT-based method can improve classification accuracy apparently. Supervised HHT-based feature extraction can achieve better classification accuracy (90%) with six and seven features, whereas unsupervised HHT-based method has lower accuracy (81.33%) with four features. Therefore, supervised HHT-based method can have better results by calculating the separability of different classes with training samples than unsupervised HHT- based method. e i Un 0% e e * e e e 5,085 ; 8 : * 2 ; + >. 3 os à e * 3 P loe e $ 075 i 9. e 3 é i = i 5 i ©. gor e e 5 i e i 4 : e gon ; oO e * 2 $ ° e 06 9. 9 058 Me MM diosa: i079 HHT-Besed FE (unsupervised) io HHT-Based FE (supervised: © 05 : : : i 0 5 16 15 20 28 30 tiumber of Feature Figure 3. Comparison of classification accuracies between HHT-based methods

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