Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture
The increasing amount of data produced in satellites poses a downlink communication problem due to the limited data rate of the downlink. This bottleneck is solved by introducing more and more processing power on-board to compress data to a satisfiable rate. This paper introduces an algorithm which...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 335 |
|---|---|
| container_issue | |
| container_start_page | 330 |
| container_title | |
| container_volume | |
| creator | Walters, K.H.G. Kokkeler, A.B.J. Gerez, S. Smit, G.J.M. |
| description | The increasing amount of data produced in satellites poses a downlink communication problem due to the limited data rate of the downlink. This bottleneck is solved by introducing more and more processing power on-board to compress data to a satisfiable rate. This paper introduces an algorithm which has been developed to compress hyperspectral images at low complexity and describes its mapping to a new hardware platform called the Xentium. It is characterized by both high flexibility as well as high processing power. After introducing the algorithm the Xentium hardware is described. The different mapping strategies are explained and a cycle estimation is derived. It turns out that the compression algorithm can indeed be efficiently mapped on a reconfigurable tile like the Xentium. An image of 1024 times 1024 with 50 bands can be compressed in about 4 seconds on a single tile. Adding more tiles gives a close to linear speedup. |
| doi_str_mv | 10.1109/AHS.2009.28 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5325435</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5325435</ieee_id><sourcerecordid>5325435</sourcerecordid><originalsourceid>FETCH-LOGICAL-i212t-329dc7f39632141341c69402e185ccb745d921c784ff0f81210be6ce34ef0f303</originalsourceid><addsrcrecordid>eNotjE1LxDAURQMiqOOsXLrJH2jNy2ezLEXtQMWFuh466atGWluSDNp_b2WEC5fDPVxCboDlAMzelfVLzhmzOS_OyBUz2iphQOoLso3xkzEGVhsli0vSNNN3Vk3jPOCPTwutlxlDnNGl0A50N7bvSP_mgDH66YuuaenTcUg-S37AjpbBffi0-seA1-S8b4eI2__ekLeH-9eqzprnx11VNpnnwFMmuO2c6YXVgoMEIcFpKxlHKJRzByNVZzk4U8i-Z30BHNgBtUMhcWXBxIbcnn49Iu7n4Mc2LHsluJJCiV-PxEo5</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Walters, K.H.G. ; Kokkeler, A.B.J. ; Gerez, S. ; Smit, G.J.M.</creator><creatorcontrib>Walters, K.H.G. ; Kokkeler, A.B.J. ; Gerez, S. ; Smit, G.J.M.</creatorcontrib><description>The increasing amount of data produced in satellites poses a downlink communication problem due to the limited data rate of the downlink. This bottleneck is solved by introducing more and more processing power on-board to compress data to a satisfiable rate. This paper introduces an algorithm which has been developed to compress hyperspectral images at low complexity and describes its mapping to a new hardware platform called the Xentium. It is characterized by both high flexibility as well as high processing power. After introducing the algorithm the Xentium hardware is described. The different mapping strategies are explained and a cycle estimation is derived. It turns out that the compression algorithm can indeed be efficiently mapped on a reconfigurable tile like the Xentium. An image of 1024 times 1024 with 50 bands can be compressed in about 4 seconds on a single tile. Adding more tiles gives a close to linear speedup.</description><identifier>ISBN: 0769537146</identifier><identifier>ISBN: 9780769537146</identifier><identifier>DOI: 10.1109/AHS.2009.28</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptive systems ; Compression algorithms ; Downlink ; Energy consumption ; Hardware ; Hyperspectral imaging ; Hyperspectral sensors ; Image coding ; NASA ; Tiles</subject><ispartof>2009 NASA/ESA Conference on Adaptive Hardware and Systems, 2009, p.330-335</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5325435$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5325435$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Walters, K.H.G.</creatorcontrib><creatorcontrib>Kokkeler, A.B.J.</creatorcontrib><creatorcontrib>Gerez, S.</creatorcontrib><creatorcontrib>Smit, G.J.M.</creatorcontrib><title>Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture</title><title>2009 NASA/ESA Conference on Adaptive Hardware and Systems</title><addtitle>AHS</addtitle><description>The increasing amount of data produced in satellites poses a downlink communication problem due to the limited data rate of the downlink. This bottleneck is solved by introducing more and more processing power on-board to compress data to a satisfiable rate. This paper introduces an algorithm which has been developed to compress hyperspectral images at low complexity and describes its mapping to a new hardware platform called the Xentium. It is characterized by both high flexibility as well as high processing power. After introducing the algorithm the Xentium hardware is described. The different mapping strategies are explained and a cycle estimation is derived. It turns out that the compression algorithm can indeed be efficiently mapped on a reconfigurable tile like the Xentium. An image of 1024 times 1024 with 50 bands can be compressed in about 4 seconds on a single tile. Adding more tiles gives a close to linear speedup.</description><subject>Adaptive systems</subject><subject>Compression algorithms</subject><subject>Downlink</subject><subject>Energy consumption</subject><subject>Hardware</subject><subject>Hyperspectral imaging</subject><subject>Hyperspectral sensors</subject><subject>Image coding</subject><subject>NASA</subject><subject>Tiles</subject><isbn>0769537146</isbn><isbn>9780769537146</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjE1LxDAURQMiqOOsXLrJH2jNy2ezLEXtQMWFuh466atGWluSDNp_b2WEC5fDPVxCboDlAMzelfVLzhmzOS_OyBUz2iphQOoLso3xkzEGVhsli0vSNNN3Vk3jPOCPTwutlxlDnNGl0A50N7bvSP_mgDH66YuuaenTcUg-S37AjpbBffi0-seA1-S8b4eI2__ekLeH-9eqzprnx11VNpnnwFMmuO2c6YXVgoMEIcFpKxlHKJRzByNVZzk4U8i-Z30BHNgBtUMhcWXBxIbcnn49Iu7n4Mc2LHsluJJCiV-PxEo5</recordid><startdate>20090101</startdate><enddate>20090101</enddate><creator>Walters, K.H.G.</creator><creator>Kokkeler, A.B.J.</creator><creator>Gerez, S.</creator><creator>Smit, G.J.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20090101</creationdate><title>Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture</title><author>Walters, K.H.G. ; Kokkeler, A.B.J. ; Gerez, S. ; Smit, G.J.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i212t-329dc7f39632141341c69402e185ccb745d921c784ff0f81210be6ce34ef0f303</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adaptive systems</topic><topic>Compression algorithms</topic><topic>Downlink</topic><topic>Energy consumption</topic><topic>Hardware</topic><topic>Hyperspectral imaging</topic><topic>Hyperspectral sensors</topic><topic>Image coding</topic><topic>NASA</topic><topic>Tiles</topic><toplevel>online_resources</toplevel><creatorcontrib>Walters, K.H.G.</creatorcontrib><creatorcontrib>Kokkeler, A.B.J.</creatorcontrib><creatorcontrib>Gerez, S.</creatorcontrib><creatorcontrib>Smit, G.J.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>Walters, K.H.G.</au><au>Kokkeler, A.B.J.</au><au>Gerez, S.</au><au>Smit, G.J.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture</atitle><btitle>2009 NASA/ESA Conference on Adaptive Hardware and Systems</btitle><stitle>AHS</stitle><date>2009-01-01</date><risdate>2009</risdate><spage>330</spage><epage>335</epage><pages>330-335</pages><isbn>0769537146</isbn><isbn>9780769537146</isbn><abstract>The increasing amount of data produced in satellites poses a downlink communication problem due to the limited data rate of the downlink. This bottleneck is solved by introducing more and more processing power on-board to compress data to a satisfiable rate. This paper introduces an algorithm which has been developed to compress hyperspectral images at low complexity and describes its mapping to a new hardware platform called the Xentium. It is characterized by both high flexibility as well as high processing power. After introducing the algorithm the Xentium hardware is described. The different mapping strategies are explained and a cycle estimation is derived. It turns out that the compression algorithm can indeed be efficiently mapped on a reconfigurable tile like the Xentium. An image of 1024 times 1024 with 50 bands can be compressed in about 4 seconds on a single tile. Adding more tiles gives a close to linear speedup.</abstract><pub>IEEE</pub><doi>10.1109/AHS.2009.28</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 0769537146 |
| ispartof | 2009 NASA/ESA Conference on Adaptive Hardware and Systems, 2009, p.330-335 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5325435 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive systems Compression algorithms Downlink Energy consumption Hardware Hyperspectral imaging Hyperspectral sensors Image coding NASA Tiles |
| title | Low-Complexity Hyperspectral Image Compression on a Multi-tiled Architecture |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T10%3A23%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Low-Complexity%20Hyperspectral%20Image%20Compression%20on%20a%20Multi-tiled%20Architecture&rft.btitle=2009%20NASA/ESA%20Conference%20on%20Adaptive%20Hardware%20and%20Systems&rft.au=Walters,%20K.H.G.&rft.date=2009-01-01&rft.spage=330&rft.epage=335&rft.pages=330-335&rft.isbn=0769537146&rft.isbn_list=9780769537146&rft_id=info:doi/10.1109/AHS.2009.28&rft_dat=%3Cieee_6IE%3E5325435%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5325435&rfr_iscdi=true |