Automated identification of land cover type using multispectral satellite images
Samo za registrovane korisnike
2016
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Detection of specific terrain features and vegetation, referenced as a landscape classification, is an important component in the management and planning of natural resources. The different land types, man-made materials in natural backgrounds and vegetation cultures can be distinguished by their reflectance. Although remote sensing technology has great potential for acquisition of detailed and accurate information of landscape regions, the determination of land-use data with high accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. Therefore, remote sensing with multi-spectral or/and hyper spectral data derived from various satellites in combination with topographic variables is a valuable tool in landscape type classification. The different methods based on reflectance data from multi-spectral Landsat satellite image sets are used for automatic landscape type recognition.... In order to characterize reflectance of landscape types represented in an image, construction of a multi-spectral descriptor, as a vector of acquired reflectance values by wavelength bands, is proposed. The applied algorithms for landscape type classification (artificial neural network, support vector machines and logistic regression) have been analysed and results are compared and discussed in terms of accuracy and time of execution.
Ključne reči:
Support vector machines / Remote sensing / Neural networks / Multispectral images / Logistic regression / Landscape classificationIzvor:
Energy and Buildings, 2016, 115, 131-137Izdavač:
- Elsevier Science Sa, Lausanne
Finansiranje / projekti:
- Razvoj digitalnih tehnologija i umreženih servisa u sistemima sa ugrađenim elektronskim komponentama (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-44009)
DOI: 10.1016/j.enbuild.2015.06.011
ISSN: 0378-7788
WoS: 000373750300016
Scopus: 2-s2.0-84932154136
Institucija/grupa
Inovacioni centarTY - JOUR AU - Stević, Dragan AU - Hut, Igor AU - Dojcinović, Nikola AU - Joković, Jugoslav PY - 2016 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2331 AB - Detection of specific terrain features and vegetation, referenced as a landscape classification, is an important component in the management and planning of natural resources. The different land types, man-made materials in natural backgrounds and vegetation cultures can be distinguished by their reflectance. Although remote sensing technology has great potential for acquisition of detailed and accurate information of landscape regions, the determination of land-use data with high accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. Therefore, remote sensing with multi-spectral or/and hyper spectral data derived from various satellites in combination with topographic variables is a valuable tool in landscape type classification. The different methods based on reflectance data from multi-spectral Landsat satellite image sets are used for automatic landscape type recognition. In order to characterize reflectance of landscape types represented in an image, construction of a multi-spectral descriptor, as a vector of acquired reflectance values by wavelength bands, is proposed. The applied algorithms for landscape type classification (artificial neural network, support vector machines and logistic regression) have been analysed and results are compared and discussed in terms of accuracy and time of execution. PB - Elsevier Science Sa, Lausanne T2 - Energy and Buildings T1 - Automated identification of land cover type using multispectral satellite images EP - 137 SP - 131 VL - 115 DO - 10.1016/j.enbuild.2015.06.011 ER -
@article{ author = "Stević, Dragan and Hut, Igor and Dojcinović, Nikola and Joković, Jugoslav", year = "2016", abstract = "Detection of specific terrain features and vegetation, referenced as a landscape classification, is an important component in the management and planning of natural resources. The different land types, man-made materials in natural backgrounds and vegetation cultures can be distinguished by their reflectance. Although remote sensing technology has great potential for acquisition of detailed and accurate information of landscape regions, the determination of land-use data with high accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. Therefore, remote sensing with multi-spectral or/and hyper spectral data derived from various satellites in combination with topographic variables is a valuable tool in landscape type classification. The different methods based on reflectance data from multi-spectral Landsat satellite image sets are used for automatic landscape type recognition. In order to characterize reflectance of landscape types represented in an image, construction of a multi-spectral descriptor, as a vector of acquired reflectance values by wavelength bands, is proposed. The applied algorithms for landscape type classification (artificial neural network, support vector machines and logistic regression) have been analysed and results are compared and discussed in terms of accuracy and time of execution.", publisher = "Elsevier Science Sa, Lausanne", journal = "Energy and Buildings", title = "Automated identification of land cover type using multispectral satellite images", pages = "137-131", volume = "115", doi = "10.1016/j.enbuild.2015.06.011" }
Stević, D., Hut, I., Dojcinović, N.,& Joković, J.. (2016). Automated identification of land cover type using multispectral satellite images. in Energy and Buildings Elsevier Science Sa, Lausanne., 115, 131-137. https://doi.org/10.1016/j.enbuild.2015.06.011
Stević D, Hut I, Dojcinović N, Joković J. Automated identification of land cover type using multispectral satellite images. in Energy and Buildings. 2016;115:131-137. doi:10.1016/j.enbuild.2015.06.011 .
Stević, Dragan, Hut, Igor, Dojcinović, Nikola, Joković, Jugoslav, "Automated identification of land cover type using multispectral satellite images" in Energy and Buildings, 115 (2016):131-137, https://doi.org/10.1016/j.enbuild.2015.06.011 . .