Targeted change detection in remote sensing images
Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Ignatiev, Vladimir Trekin, Alexey Lobachev, Viktor Potapov, Georgy Burnaev, Evgeny |
| description | Recent developments in the remote sensing systems and image processing made
it possible to propose a new method of the object classification and detection
of the specific changes in the series of satellite Earth images (so called
targeted change detection). In this paper we propose a formal problem statement
that allows to use effectively the deep learning approach to analyze
time-dependent series of remote sensing images. We also introduce a new
framework for the development of deep learning models for targeted change
detection and demonstrate some cases of business applications it can be used
for. |
| doi_str_mv | 10.48550/arxiv.1803.05482 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1803_05482</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1803_05482</sourcerecordid><originalsourceid>FETCH-LOGICAL-a672-b2498a6aaa223ca08db34c69e877a510ee758d4288f2f3b4eb9524c7082c56b63</originalsourceid><addsrcrecordid>eNotzs1OwzAQBGBfOKCWB-CEXyDBWf9tjlVFAakSl9yjtbMJllq3ciIEb99SOI3mMvMJ8dio2qC16pnKd_qqG1S6VtYg3AvoqEy88CDjJ-WJ5XAtcUmnLFOWhY-nheXMeU55kulIE89rcTfSYeaH_1yJbvfSbd-q_cfr-3azr8h5qAKYFskREYCOpHAI2kTXMnpPtlHM3uJgAHGEUQfDobVgolcI0brg9Eo8_c3e0P25XN_LT_-L7294fQHKxz5z</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Targeted change detection in remote sensing images</title><source>arXiv.org</source><creator>Ignatiev, Vladimir ; Trekin, Alexey ; Lobachev, Viktor ; Potapov, Georgy ; Burnaev, Evgeny</creator><creatorcontrib>Ignatiev, Vladimir ; Trekin, Alexey ; Lobachev, Viktor ; Potapov, Georgy ; Burnaev, Evgeny</creatorcontrib><description>Recent developments in the remote sensing systems and image processing made
it possible to propose a new method of the object classification and detection
of the specific changes in the series of satellite Earth images (so called
targeted change detection). In this paper we propose a formal problem statement
that allows to use effectively the deep learning approach to analyze
time-dependent series of remote sensing images. We also introduce a new
framework for the development of deep learning models for targeted change
detection and demonstrate some cases of business applications it can be used
for.</description><identifier>DOI: 10.48550/arxiv.1803.05482</identifier><language>eng</language><subject>Computer Science - Computational Engineering, Finance, and Science ; Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2018-03</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1803.05482$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1803.05482$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ignatiev, Vladimir</creatorcontrib><creatorcontrib>Trekin, Alexey</creatorcontrib><creatorcontrib>Lobachev, Viktor</creatorcontrib><creatorcontrib>Potapov, Georgy</creatorcontrib><creatorcontrib>Burnaev, Evgeny</creatorcontrib><title>Targeted change detection in remote sensing images</title><description>Recent developments in the remote sensing systems and image processing made
it possible to propose a new method of the object classification and detection
of the specific changes in the series of satellite Earth images (so called
targeted change detection). In this paper we propose a formal problem statement
that allows to use effectively the deep learning approach to analyze
time-dependent series of remote sensing images. We also introduce a new
framework for the development of deep learning models for targeted change
detection and demonstrate some cases of business applications it can be used
for.</description><subject>Computer Science - Computational Engineering, Finance, and Science</subject><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzs1OwzAQBGBfOKCWB-CEXyDBWf9tjlVFAakSl9yjtbMJllq3ciIEb99SOI3mMvMJ8dio2qC16pnKd_qqG1S6VtYg3AvoqEy88CDjJ-WJ5XAtcUmnLFOWhY-nheXMeU55kulIE89rcTfSYeaH_1yJbvfSbd-q_cfr-3azr8h5qAKYFskREYCOpHAI2kTXMnpPtlHM3uJgAHGEUQfDobVgolcI0brg9Eo8_c3e0P25XN_LT_-L7294fQHKxz5z</recordid><startdate>20180314</startdate><enddate>20180314</enddate><creator>Ignatiev, Vladimir</creator><creator>Trekin, Alexey</creator><creator>Lobachev, Viktor</creator><creator>Potapov, Georgy</creator><creator>Burnaev, Evgeny</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20180314</creationdate><title>Targeted change detection in remote sensing images</title><author>Ignatiev, Vladimir ; Trekin, Alexey ; Lobachev, Viktor ; Potapov, Georgy ; Burnaev, Evgeny</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-b2498a6aaa223ca08db34c69e877a510ee758d4288f2f3b4eb9524c7082c56b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Computer Science - Computational Engineering, Finance, and Science</topic><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Ignatiev, Vladimir</creatorcontrib><creatorcontrib>Trekin, Alexey</creatorcontrib><creatorcontrib>Lobachev, Viktor</creatorcontrib><creatorcontrib>Potapov, Georgy</creatorcontrib><creatorcontrib>Burnaev, Evgeny</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ignatiev, Vladimir</au><au>Trekin, Alexey</au><au>Lobachev, Viktor</au><au>Potapov, Georgy</au><au>Burnaev, Evgeny</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Targeted change detection in remote sensing images</atitle><date>2018-03-14</date><risdate>2018</risdate><abstract>Recent developments in the remote sensing systems and image processing made
it possible to propose a new method of the object classification and detection
of the specific changes in the series of satellite Earth images (so called
targeted change detection). In this paper we propose a formal problem statement
that allows to use effectively the deep learning approach to analyze
time-dependent series of remote sensing images. We also introduce a new
framework for the development of deep learning models for targeted change
detection and demonstrate some cases of business applications it can be used
for.</abstract><doi>10.48550/arxiv.1803.05482</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.1803.05482 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_1803_05482 |
| source | arXiv.org |
| subjects | Computer Science - Computational Engineering, Finance, and Science Computer Science - Computer Vision and Pattern Recognition |
| title | Targeted change detection in remote sensing images |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T14%3A05%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Targeted%20change%20detection%20in%20remote%20sensing%20images&rft.au=Ignatiev,%20Vladimir&rft.date=2018-03-14&rft_id=info:doi/10.48550/arxiv.1803.05482&rft_dat=%3Carxiv_GOX%3E1803_05482%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |