An experimetal study on Content-based Image Classification for Satellite Image Databases
Current art uses metad8E associated with satellite images to facilitate their retrieval from image repositories. Typical metadSD are geographic location, time, and d7 type. Because themetad7E d not indkS1U which regions within an image are obscured bycloudq retrieval with suchmetad7W may prodWk an i...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.23.1531 2023-05-15T17:35:39+02:00 An experimetal study on Content-based Image Classification for Satellite Image Databases Francisco Artigas Richard Holowczak Soon Ae Chun June-Suh Cho Junesuh Cho Harold Stone The Pennsylvania State University CiteSeerX Archives 2001 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.1531 http://cimic.rutgers.edu/~soon/papers/nec01.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.1531 http://cimic.rutgers.edu/~soon/papers/nec01.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://cimic.rutgers.edu/~soon/papers/nec01.pdf text 2001 ftciteseerx 2016-01-07T18:39:52Z Current art uses metad8E associated with satellite images to facilitate their retrieval from image repositories. Typical metadSD are geographic location, time, and d7 type. Because themetad7E d not indkS1U which regions within an image are obscured bycloudq retrieval with suchmetad7W may prodWk an image within which the region of interest (ROI) for the user is not visible. We report a system that can automaticallydutomati whether an ROI is visible in the image, and can incorporate this into the metadSS for ind0811Wfi images to enhance searching capability. The goal is to annotate each image withmetad0U regard0U a number of ROIs. An experiment with the system annotated 236 AVHRR images of the North Atlantic from a 5-month viewing period with dhWkD788Wfi that expressed the visibility of an ROI centered on Long Island For ground truth, weused the classifications of three human subjects to d18D7EWfi visibility of the same region of interest, and labeled the ROI with the majorityd17DD8W of the three subjects. Partialcloud covermad the humandW87Uq1Wfi88S subjective,and resulted ind isagreements among the subjects. Using randSUk selected 1 training subsets of the images, wefound the two images whose regions were most like those in images for which the LongIsland region was visible. For training subsets, thedW888D7Wfi0 dUqW ed from the two best images prodWkD average Recalland Precision retrieval results jointly in the 75 percent to 80 percent region. Descriptorsdscr ed from those same two images for the test subsets, also prodWk0 average Recalland Precision results that jointly fell in the 75 to 80 percent region. 1 Introducti6 Trad7k8Wfi8 search and retrieval systems for remotely sensed d ata provid facilities to query by sensor characteristics (sensor type, channel or ban. Text North Atlantic Unknown Long Island |
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ftciteseerx |
language |
English |
description |
Current art uses metad8E associated with satellite images to facilitate their retrieval from image repositories. Typical metadSD are geographic location, time, and d7 type. Because themetad7E d not indkS1U which regions within an image are obscured bycloudq retrieval with suchmetad7W may prodWk an image within which the region of interest (ROI) for the user is not visible. We report a system that can automaticallydutomati whether an ROI is visible in the image, and can incorporate this into the metadSS for ind0811Wfi images to enhance searching capability. The goal is to annotate each image withmetad0U regard0U a number of ROIs. An experiment with the system annotated 236 AVHRR images of the North Atlantic from a 5-month viewing period with dhWkD788Wfi that expressed the visibility of an ROI centered on Long Island For ground truth, weused the classifications of three human subjects to d18D7EWfi visibility of the same region of interest, and labeled the ROI with the majorityd17DD8W of the three subjects. Partialcloud covermad the humandW87Uq1Wfi88S subjective,and resulted ind isagreements among the subjects. Using randSUk selected 1 training subsets of the images, wefound the two images whose regions were most like those in images for which the LongIsland region was visible. For training subsets, thedW888D7Wfi0 dUqW ed from the two best images prodWkD average Recalland Precision retrieval results jointly in the 75 percent to 80 percent region. Descriptorsdscr ed from those same two images for the test subsets, also prodWk0 average Recalland Precision results that jointly fell in the 75 to 80 percent region. 1 Introducti6 Trad7k8Wfi8 search and retrieval systems for remotely sensed d ata provid facilities to query by sensor characteristics (sensor type, channel or ban. |
author2 |
The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Francisco Artigas Richard Holowczak Soon Ae Chun June-Suh Cho Junesuh Cho Harold Stone |
spellingShingle |
Francisco Artigas Richard Holowczak Soon Ae Chun June-Suh Cho Junesuh Cho Harold Stone An experimetal study on Content-based Image Classification for Satellite Image Databases |
author_facet |
Francisco Artigas Richard Holowczak Soon Ae Chun June-Suh Cho Junesuh Cho Harold Stone |
author_sort |
Francisco Artigas |
title |
An experimetal study on Content-based Image Classification for Satellite Image Databases |
title_short |
An experimetal study on Content-based Image Classification for Satellite Image Databases |
title_full |
An experimetal study on Content-based Image Classification for Satellite Image Databases |
title_fullStr |
An experimetal study on Content-based Image Classification for Satellite Image Databases |
title_full_unstemmed |
An experimetal study on Content-based Image Classification for Satellite Image Databases |
title_sort |
experimetal study on content-based image classification for satellite image databases |
publishDate |
2001 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.1531 http://cimic.rutgers.edu/~soon/papers/nec01.pdf |
geographic |
Long Island |
geographic_facet |
Long Island |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
http://cimic.rutgers.edu/~soon/papers/nec01.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.1531 http://cimic.rutgers.edu/~soon/papers/nec01.pdf |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766134886812352512 |