Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery
The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotempora...
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 | Sani, Depanshu Mahato, Sandeep Sirohi, Parichya Anand, Saket Arora, Gaurav Devshali, Charu Chandra Jayaraman, T |
| description | The integration of the modern Machine Learning (ML) models into remote
sensing and agriculture has expanded the scope of the application of satellite
images in the agriculture domain. In this paper, we present how the accuracy of
crop type identification improves as we move from
medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution
(HSTR) satellite images. We further demonstrate that high spectral resolution
in satellite imagery can improve prediction performance for low spatial and
temporal resolutions (LSTR) images. The F1-score is increased by 7% when using
multispectral data of MSTR images as compared to the best results obtained from
HSTR images. Similarly, when crop season based time series of multispectral
data is used we observe an increase of 1.2% in the F1-score. The outcome
motivates further advancements in the field of synthetic band generation. |
| doi_str_mv | 10.48550/arxiv.2205.03104 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2205_03104</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2205_03104</sourcerecordid><originalsourceid>FETCH-LOGICAL-a674-b43e9f0546926c373ea521f41d67829cd093806445e7ece67eb0f661428898f53</originalsourceid><addsrcrecordid>eNotUM1OhDAY7MWDWX0AT_YBBAv9AbxtiKubbGIi3Mm38HVtUlpS0Mg-vbDuaTKTyUxmCHlIWCxyKdkzhF_zE6cpkzHjCRO35FwGP9B6HpDuO3ST0aaFyXhHtQ-06sHaL2874050B6EfX-jWgZ3Pq1ANixPsE62xH3wAS8F1i4rttJJPHL39XrNGahytYEJrzbQU9XDCMN-RGw12xPsrbki9e63L9-jw8bYvt4cIVCaio-BYaCaFKlLV8owjyDTRIulUlqdF27GC50wJITHDFlWGR6aVSkSa50WuJd-Qx__Yy_hmCKaHMDfrCc3lBP4Hp_RYMg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery</title><source>arXiv.org</source><creator>Sani, Depanshu ; Mahato, Sandeep ; Sirohi, Parichya ; Anand, Saket ; Arora, Gaurav ; Devshali, Charu Chandra ; Jayaraman, T</creator><creatorcontrib>Sani, Depanshu ; Mahato, Sandeep ; Sirohi, Parichya ; Anand, Saket ; Arora, Gaurav ; Devshali, Charu Chandra ; Jayaraman, T</creatorcontrib><description>The integration of the modern Machine Learning (ML) models into remote
sensing and agriculture has expanded the scope of the application of satellite
images in the agriculture domain. In this paper, we present how the accuracy of
crop type identification improves as we move from
medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution
(HSTR) satellite images. We further demonstrate that high spectral resolution
in satellite imagery can improve prediction performance for low spatial and
temporal resolutions (LSTR) images. The F1-score is increased by 7% when using
multispectral data of MSTR images as compared to the best results obtained from
HSTR images. Similarly, when crop season based time series of multispectral
data is used we observe an increase of 1.2% in the F1-score. The outcome
motivates further advancements in the field of synthetic band generation.</description><identifier>DOI: 10.48550/arxiv.2205.03104</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Learning</subject><creationdate>2022-05</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>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2205.03104$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2205.03104$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Sani, Depanshu</creatorcontrib><creatorcontrib>Mahato, Sandeep</creatorcontrib><creatorcontrib>Sirohi, Parichya</creatorcontrib><creatorcontrib>Anand, Saket</creatorcontrib><creatorcontrib>Arora, Gaurav</creatorcontrib><creatorcontrib>Devshali, Charu Chandra</creatorcontrib><creatorcontrib>Jayaraman, T</creatorcontrib><title>Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery</title><description>The integration of the modern Machine Learning (ML) models into remote
sensing and agriculture has expanded the scope of the application of satellite
images in the agriculture domain. In this paper, we present how the accuracy of
crop type identification improves as we move from
medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution
(HSTR) satellite images. We further demonstrate that high spectral resolution
in satellite imagery can improve prediction performance for low spatial and
temporal resolutions (LSTR) images. The F1-score is increased by 7% when using
multispectral data of MSTR images as compared to the best results obtained from
HSTR images. Similarly, when crop season based time series of multispectral
data is used we observe an increase of 1.2% in the F1-score. The outcome
motivates further advancements in the field of synthetic band generation.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotUM1OhDAY7MWDWX0AT_YBBAv9AbxtiKubbGIi3Mm38HVtUlpS0Mg-vbDuaTKTyUxmCHlIWCxyKdkzhF_zE6cpkzHjCRO35FwGP9B6HpDuO3ST0aaFyXhHtQ-06sHaL2874050B6EfX-jWgZ3Pq1ANixPsE62xH3wAS8F1i4rttJJPHL39XrNGahytYEJrzbQU9XDCMN-RGw12xPsrbki9e63L9-jw8bYvt4cIVCaio-BYaCaFKlLV8owjyDTRIulUlqdF27GC50wJITHDFlWGR6aVSkSa50WuJd-Qx__Yy_hmCKaHMDfrCc3lBP4Hp_RYMg</recordid><startdate>20220506</startdate><enddate>20220506</enddate><creator>Sani, Depanshu</creator><creator>Mahato, Sandeep</creator><creator>Sirohi, Parichya</creator><creator>Anand, Saket</creator><creator>Arora, Gaurav</creator><creator>Devshali, Charu Chandra</creator><creator>Jayaraman, T</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220506</creationdate><title>Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery</title><author>Sani, Depanshu ; Mahato, Sandeep ; Sirohi, Parichya ; Anand, Saket ; Arora, Gaurav ; Devshali, Charu Chandra ; Jayaraman, T</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-b43e9f0546926c373ea521f41d67829cd093806445e7ece67eb0f661428898f53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Sani, Depanshu</creatorcontrib><creatorcontrib>Mahato, Sandeep</creatorcontrib><creatorcontrib>Sirohi, Parichya</creatorcontrib><creatorcontrib>Anand, Saket</creatorcontrib><creatorcontrib>Arora, Gaurav</creatorcontrib><creatorcontrib>Devshali, Charu Chandra</creatorcontrib><creatorcontrib>Jayaraman, T</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sani, Depanshu</au><au>Mahato, Sandeep</au><au>Sirohi, Parichya</au><au>Anand, Saket</au><au>Arora, Gaurav</au><au>Devshali, Charu Chandra</au><au>Jayaraman, T</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery</atitle><date>2022-05-06</date><risdate>2022</risdate><abstract>The integration of the modern Machine Learning (ML) models into remote
sensing and agriculture has expanded the scope of the application of satellite
images in the agriculture domain. In this paper, we present how the accuracy of
crop type identification improves as we move from
medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution
(HSTR) satellite images. We further demonstrate that high spectral resolution
in satellite imagery can improve prediction performance for low spatial and
temporal resolutions (LSTR) images. The F1-score is increased by 7% when using
multispectral data of MSTR images as compared to the best results obtained from
HSTR images. Similarly, when crop season based time series of multispectral
data is used we observe an increase of 1.2% in the F1-score. The outcome
motivates further advancements in the field of synthetic band generation.</abstract><doi>10.48550/arxiv.2205.03104</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2205.03104 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2205_03104 |
| source | arXiv.org |
| subjects | Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning |
| title | Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T21%3A18%3A57IST&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=Crop%20Type%20Identification%20for%20Smallholding%20Farms:%20Analyzing%20Spatial,%20Temporal%20and%20Spectral%20Resolutions%20in%20Satellite%20Imagery&rft.au=Sani,%20Depanshu&rft.date=2022-05-06&rft_id=info:doi/10.48550/arxiv.2205.03104&rft_dat=%3Carxiv_GOX%3E2205_03104%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 |