Classification of urban areas in multi-date ERS-1 images using structural features and a neural network
Describes a new method to extract structural informations from images. The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according...
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| creator | Hagg, W. Segl, K. Sties, M. |
| description | Describes a new method to extract structural informations from images. The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according to the structures contained in SAR images. Finally they use a neural network (RBF-Network) to get a more precise classification of urban areas from SAR images. |
| doi_str_mv | 10.1109/IGARSS.1995.521091 |
| format | Conference Proceeding |
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The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according to the structures contained in SAR images. Finally they use a neural network (RBF-Network) to get a more precise classification of urban areas from SAR images.</description><identifier>ISBN: 0780325672</identifier><identifier>ISBN: 9780780325678</identifier><identifier>DOI: 10.1109/IGARSS.1995.521091</identifier><language>eng</language><publisher>IEEE</publisher><subject>Area measurement ; Data mining ; Distortion measurement ; Feature extraction ; Image resolution ; Loss measurement ; Neural networks ; Radial basis function networks ; Spatial resolution ; Urban areas</subject><ispartof>1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. 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Quantitative Remote Sensing for Science and Applications</title><addtitle>IGARSS</addtitle><description>Describes a new method to extract structural informations from images. The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according to the structures contained in SAR images. Finally they use a neural network (RBF-Network) to get a more precise classification of urban areas from SAR images.</description><subject>Area measurement</subject><subject>Data mining</subject><subject>Distortion measurement</subject><subject>Feature extraction</subject><subject>Image resolution</subject><subject>Loss measurement</subject><subject>Neural networks</subject><subject>Radial basis function networks</subject><subject>Spatial resolution</subject><subject>Urban areas</subject><isbn>0780325672</isbn><isbn>9780780325678</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1995</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotUMFqwzAUM4zBtq4_0JN_IF2eHcfOsZSuKxQGTe_lxX4u3lJ32Aljf7-wThcJCYQQYwsolwBl87Lbrg5tu4SmUUslJgfu2FOpTSmFqrV4YPOcP8oJlVKyqh_Zed1jzsEHi0O4Rn71fEwdRo6JMPMQ-WXsh1A4HIhvDm0BPFzwTJmPOcQzz0Ma7TAm7LknnMSUYHQceaQ_N9LwfU2fz-zeY59p_s8zdnzdHNdvxf59u1uv9kWoJRRe110FtaqBhCcwVtgKhHFGWNBdZRrljZAg0DmyUDpltLSdU6B0pyvfyBlb3GoDEZ2-0rQ1_ZxuT8hfQ_ZVDQ</recordid><startdate>1995</startdate><enddate>1995</enddate><creator>Hagg, W.</creator><creator>Segl, K.</creator><creator>Sties, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1995</creationdate><title>Classification of urban areas in multi-date ERS-1 images using structural features and a neural network</title><author>Hagg, W. ; Segl, K. ; Sties, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i631-f76b416561e2fe18c2c4128d82c17b4895f82312addec10d5873cbd5157b74f93</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Area measurement</topic><topic>Data mining</topic><topic>Distortion measurement</topic><topic>Feature extraction</topic><topic>Image resolution</topic><topic>Loss measurement</topic><topic>Neural networks</topic><topic>Radial basis function networks</topic><topic>Spatial resolution</topic><topic>Urban areas</topic><toplevel>online_resources</toplevel><creatorcontrib>Hagg, W.</creatorcontrib><creatorcontrib>Segl, K.</creatorcontrib><creatorcontrib>Sties, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hagg, W.</au><au>Segl, K.</au><au>Sties, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Classification of urban areas in multi-date ERS-1 images using structural features and a neural network</atitle><btitle>1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications</btitle><stitle>IGARSS</stitle><date>1995</date><risdate>1995</risdate><volume>2</volume><spage>901</spage><epage>903 vol.2</epage><pages>901-903 vol.2</pages><isbn>0780325672</isbn><isbn>9780780325678</isbn><abstract>Describes a new method to extract structural informations from images. The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according to the structures contained in SAR images. Finally they use a neural network (RBF-Network) to get a more precise classification of urban areas from SAR images.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS.1995.521091</doi><oa>free_for_read</oa></addata></record> |
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| identifier | ISBN: 0780325672 |
| ispartof | 1995 International Geoscience and Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science and Applications, 1995, Vol.2, p.901-903 vol.2 |
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| language | eng |
| recordid | cdi_ieee_primary_521091 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Area measurement Data mining Distortion measurement Feature extraction Image resolution Loss measurement Neural networks Radial basis function networks Spatial resolution Urban areas |
| title | Classification of urban areas in multi-date ERS-1 images using structural features and a neural network |
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