Hyperspectral Data Classification Using Contourlet Transform
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Hyperspectral Data Classification Using Contourlet Transform (2016)
DE PB NW RP
ISBN: 9783659848551 bzw. 3659848557, in Deutsch, LAP Lambert Academic Publishing Mrz 2016, Taschenbuch, neu, Nachdruck.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana's Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification. 100 pp. Englisch.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana's Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification. 100 pp. Englisch.
2
Hyperspectral Data Classification Using Contourlet Transform (1992)
~EN NW AB
ISBN: 9783659848551 bzw. 3659848557, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Niederlande, Lieferzeit: 5 Tage, zzgl. Versandkosten.
For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana's Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification.
For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana's Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification.
3
Hyperspectral Data Classification Using Contourlet Transform (1992)
~EN PB NW
ISBN: 9783659848551 bzw. 3659848557, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Hyperspectral Data Classification Using Contourlet Transform: For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana`s Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification. Englisch, Taschenbuch.
Hyperspectral Data Classification Using Contourlet Transform: For many years, Wavelet Transform was the major feature extraction method for image classification. Since the means of feature extraction directly affects the performance of the classification, it is vital to choose an appropriate method for different types of images. Although the Wavelet Transform provides a common method for this, recent techniques are being studied that can capture further image properties hidden from the Wavelet Transform. One of the alternatives to the Wavelet Transform is the Contourlet Transform. The Contourlet Transform performs better on detecting the smoothness along the edges which is encountered on the boundaries of smooth regions of the image. Furthermore, it has more directionality than the Wavelet counterpart that can improve classification performance significantly when the image has classes with many different directions. This work applies the Contourlet Transform and its variations to the classification of the AVIRIS image data taken from Indiana`s Indian Pine test site in June 1992. The data is hyperspectral in nature and hence this work additionally provides new benchmark results on hyperspectral data classification. Englisch, Taschenbuch.
4
Symbolbild
Hyperspectral Data Classification Using Contourlet Transform (2016)
DE PB NW RP
ISBN: 9783659848551 bzw. 3659848557, in Deutsch, Lap Lambert Academic Publishing, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, English-Book-Service Mannheim [1048135], Mannheim, Germany.
This item is printed on demand for shipment within 3 working days.
This item is printed on demand for shipment within 3 working days.
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