Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images
Thick cloud contaminations in Landsat images limit their regular usage for land applications. Based on the assumption that the neighboring spectral-similar pixels outside cloudy patches have similar temporal change patterns to the cloudy pixels, this paper presents an improved neighborhood similar p...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2012-05, Vol.9 (3), p.521-525 |
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| creator | Zhu, Xiaolin Gao, Feng Liu, Desheng Chen, Jin |
| description | Thick cloud contaminations in Landsat images limit their regular usage for land applications. Based on the assumption that the neighboring spectral-similar pixels outside cloudy patches have similar temporal change patterns to the cloudy pixels, this paper presents an improved neighborhood similar pixel interpolator (NSPI) approach to build a cloud-free imagery. NSPI approach was originally developed and tested for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. Both simulated and real cloudy images were used to evaluate the performance of the proposed method. The results show that NSPI approach can restore the reflectance of cloud-contaminated images with fewer artifact edge effects comparing to a contextual multiple linear prediction (CMLP) method. The reflectance restored by NSPI approach is more accurate especially when the cloud-free auxiliary image and cloudy image are acquired from different seasons and have different spectral characteristics. |
| doi_str_mv | 10.1109/LGRS.2011.2173290 |
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Based on the assumption that the neighboring spectral-similar pixels outside cloudy patches have similar temporal change patterns to the cloudy pixels, this paper presents an improved neighborhood similar pixel interpolator (NSPI) approach to build a cloud-free imagery. NSPI approach was originally developed and tested for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. Both simulated and real cloudy images were used to evaluate the performance of the proposed method. The results show that NSPI approach can restore the reflectance of cloud-contaminated images with fewer artifact edge effects comparing to a contextual multiple linear prediction (CMLP) method. The reflectance restored by NSPI approach is more accurate especially when the cloud-free auxiliary image and cloudy image are acquired from different seasons and have different spectral characteristics.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2011.2173290</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>cloud cover ; Cloud removal ; Clouds ; Earth ; image analysis ; Image edge detection ; image processing ; Image restoration ; Land surface ; Landsat ; Linear prediction ; Pixels ; prediction ; reflectance ; Reflectivity ; Remote sensing ; Satellites ; Simulation ; Spectra ; spectral analysis ; temporal variation ; Utilities</subject><ispartof>IEEE geoscience and remote sensing letters, 2012-05, Vol.9 (3), p.521-525</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-6d0b1622a9e7fd680c9ec8c4a5d9aae4631543743ee0714cd9420f81ac60d1343</citedby><cites>FETCH-LOGICAL-c414t-6d0b1622a9e7fd680c9ec8c4a5d9aae4631543743ee0714cd9420f81ac60d1343</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6095313$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6095313$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhu, Xiaolin</creatorcontrib><creatorcontrib>Gao, Feng</creatorcontrib><creatorcontrib>Liu, Desheng</creatorcontrib><creatorcontrib>Chen, Jin</creatorcontrib><title>Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Thick cloud contaminations in Landsat images limit their regular usage for land applications. Based on the assumption that the neighboring spectral-similar pixels outside cloudy patches have similar temporal change patterns to the cloudy pixels, this paper presents an improved neighborhood similar pixel interpolator (NSPI) approach to build a cloud-free imagery. NSPI approach was originally developed and tested for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. Both simulated and real cloudy images were used to evaluate the performance of the proposed method. The results show that NSPI approach can restore the reflectance of cloud-contaminated images with fewer artifact edge effects comparing to a contextual multiple linear prediction (CMLP) method. The reflectance restored by NSPI approach is more accurate especially when the cloud-free auxiliary image and cloudy image are acquired from different seasons and have different spectral characteristics.</description><subject>cloud cover</subject><subject>Cloud removal</subject><subject>Clouds</subject><subject>Earth</subject><subject>image analysis</subject><subject>Image edge detection</subject><subject>image processing</subject><subject>Image restoration</subject><subject>Land surface</subject><subject>Landsat</subject><subject>Linear prediction</subject><subject>Pixels</subject><subject>prediction</subject><subject>reflectance</subject><subject>Reflectivity</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Simulation</subject><subject>Spectra</subject><subject>spectral analysis</subject><subject>temporal variation</subject><subject>Utilities</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqFkU9vEzEQxVcIJErhAyAOWFzgsmHG9nrtYxVBiRT-qGklbpa7nk1cdtfB3iD49jhKxYEDnGZG83ujN3pV9RxhgQjm7fryarPggLjg2Apu4EF1hk2ja2hafHjsZVM3Rn99XD3J-Q6AS63bs4o-Rh_6QJ59orDd3ca0i9GzTRjD4BL7En7SwFbTTGkfBzfHxC72-xRdt2N9Ga5ojD_CtGXXu9B9Y8shHnxmYWJrN_nsZrYa3Zby0-pR74ZMz-7reXXz_t318kO9_ny5Wl6s606inGvl4RYV585Q23uloTPU6U66xhvnSCpR3hCtFETQouy8kRx6ja5T4FFIcV69Pt0tFr8fKM92DLmjYXATxUO2RmnUHJUp5Jt_kti2IJRC0_wfVUoorkHqgr76C72LhzSVl63hShotGygQnqAuxZwT9XafwujSL4tgj2HaY5j2GKa9D7NoXpw0gYj-8AqKPRRl-_K07V20bptCtjebom8AQHHBufgNlRCi0A</recordid><startdate>20120501</startdate><enddate>20120501</enddate><creator>Zhu, Xiaolin</creator><creator>Gao, Feng</creator><creator>Liu, Desheng</creator><creator>Chen, Jin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Based on the assumption that the neighboring spectral-similar pixels outside cloudy patches have similar temporal change patterns to the cloudy pixels, this paper presents an improved neighborhood similar pixel interpolator (NSPI) approach to build a cloud-free imagery. NSPI approach was originally developed and tested for filling gaps due to the Landsat ETM+ Scan Line Corrector (SLC)-off problem. Both simulated and real cloudy images were used to evaluate the performance of the proposed method. The results show that NSPI approach can restore the reflectance of cloud-contaminated images with fewer artifact edge effects comparing to a contextual multiple linear prediction (CMLP) method. The reflectance restored by NSPI approach is more accurate especially when the cloud-free auxiliary image and cloudy image are acquired from different seasons and have different spectral characteristics.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2011.2173290</doi><tpages>5</tpages></addata></record> |
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| subjects | cloud cover Cloud removal Clouds Earth image analysis Image edge detection image processing Image restoration Land surface Landsat Linear prediction Pixels prediction reflectance Reflectivity Remote sensing Satellites Simulation Spectra spectral analysis temporal variation Utilities |
| title | Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images |
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