BibTeX
@article{IJASEIT13102,
author = {Ayom Widipaminto and Yohanes Fridolin Hestrio and Donna Monica and Yuvita Dian Safitri and Dedi Irawadi and - Rokhmatuloh and Djoko Triyono and Erna Sri Adiningsih},
title = {Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {11},
number = {5},
year = {2021},
pages = {1897--1902},
keywords = {Material identification; band combination; reflectance responses.},
abstract = {Land monitoring requires remote sensing data, which varies in its spectral and spatial resolution. Remote sensing data with the high spatial resolution is especially needed for urban monitoring. However, high spatial resolution data is usually expensive with limited coverage and complex analysis. This paper aims to find the most efficient way to do urban monitoring, specifically surface material identification. In material identification, the distinctive feature that can be used to differentiate one material surface from one another is its reflectance responses. This leads to a question of which absorption features are significant to different surface materials, especially roofing materials, and which absorption features are not discriminant enough to be used at classification. This paper proposed a machine learning-based identification of roof material types using band combinations as classification features. The experiment was done on Pleiades data, multispectral satellite imagery with very high spatial resolution. We first calculated the image’s reflectance values for each band and then grouped them based on their spectral range, yielding 11 possible combinations as the classification features. The experiment found that reflectance responses for band Red and NIR are the most distinctive trait of a material type and thus sufficient for material identification. We minimized the number of spectral responses used in material identification down to two bands, which can help the data collection and processing of material identification easier, cheaper, and less time-cost. Our experiment yields overall accuracy of 0.9959, with a computational time of 19.72 seconds.},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13102},
doi = {10.18517/ijaseit.11.5.13102}
}
EndNote
%A Widipaminto, Ayom
%A Hestrio, Yohanes Fridolin
%A Monica, Donna
%A Safitri, Yuvita Dian
%A Irawadi, Dedi
%A Rokhmatuloh, -
%A Triyono, Djoko
%A Adiningsih, Erna Sri
%D 2021
%T Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification
%B 2021
%9 Material identification; band combination; reflectance responses.
%! Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification
%K Material identification; band combination; reflectance responses.
%X Land monitoring requires remote sensing data, which varies in its spectral and spatial resolution. Remote sensing data with the high spatial resolution is especially needed for urban monitoring. However, high spatial resolution data is usually expensive with limited coverage and complex analysis. This paper aims to find the most efficient way to do urban monitoring, specifically surface material identification. In material identification, the distinctive feature that can be used to differentiate one material surface from one another is its reflectance responses. This leads to a question of which absorption features are significant to different surface materials, especially roofing materials, and which absorption features are not discriminant enough to be used at classification. This paper proposed a machine learning-based identification of roof material types using band combinations as classification features. The experiment was done on Pleiades data, multispectral satellite imagery with very high spatial resolution. We first calculated the image’s reflectance values for each band and then grouped them based on their spectral range, yielding 11 possible combinations as the classification features. The experiment found that reflectance responses for band Red and NIR are the most distinctive trait of a material type and thus sufficient for material identification. We minimized the number of spectral responses used in material identification down to two bands, which can help the data collection and processing of material identification easier, cheaper, and less time-cost. Our experiment yields overall accuracy of 0.9959, with a computational time of 19.72 seconds.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13102
%R doi:10.18517/ijaseit.11.5.13102
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 5
%@ 2088-5334
IEEE
Ayom Widipaminto,Yohanes Fridolin Hestrio,Donna Monica,Yuvita Dian Safitri,Dedi Irawadi,- Rokhmatuloh,Djoko Triyono and Erna Sri Adiningsih,"Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1897-1902, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.13102.
RefMan/ProCite (RIS)
TY - JOUR
AU - Widipaminto, Ayom
AU - Hestrio, Yohanes Fridolin
AU - Monica, Donna
AU - Safitri, Yuvita Dian
AU - Irawadi, Dedi
AU - Rokhmatuloh, -
AU - Triyono, Djoko
AU - Adiningsih, Erna Sri
PY - 2021
TI - Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2 - 2021
SP - 1897
EP - 1902
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Material identification; band combination; reflectance responses.
N2 - Land monitoring requires remote sensing data, which varies in its spectral and spatial resolution. Remote sensing data with the high spatial resolution is especially needed for urban monitoring. However, high spatial resolution data is usually expensive with limited coverage and complex analysis. This paper aims to find the most efficient way to do urban monitoring, specifically surface material identification. In material identification, the distinctive feature that can be used to differentiate one material surface from one another is its reflectance responses. This leads to a question of which absorption features are significant to different surface materials, especially roofing materials, and which absorption features are not discriminant enough to be used at classification. This paper proposed a machine learning-based identification of roof material types using band combinations as classification features. The experiment was done on Pleiades data, multispectral satellite imagery with very high spatial resolution. We first calculated the image’s reflectance values for each band and then grouped them based on their spectral range, yielding 11 possible combinations as the classification features. The experiment found that reflectance responses for band Red and NIR are the most distinctive trait of a material type and thus sufficient for material identification. We minimized the number of spectral responses used in material identification down to two bands, which can help the data collection and processing of material identification easier, cheaper, and less time-cost. Our experiment yields overall accuracy of 0.9959, with a computational time of 19.72 seconds.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13102
DO - 10.18517/ijaseit.11.5.13102
RefWorks
RT Journal Article
ID 13102
A1 Widipaminto, Ayom
A1 Hestrio, Yohanes Fridolin
A1 Monica, Donna
A1 Safitri, Yuvita Dian
A1 Irawadi, Dedi
A1 Rokhmatuloh, -
A1 Triyono, Djoko
A1 Adiningsih, Erna Sri
T1 Utilization of Band Combination for Feature Selection in Machine Learning-Based Roof Material Types Identification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1897
OP 1902
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Material identification; band combination; reflectance responses.
AB Land monitoring requires remote sensing data, which varies in its spectral and spatial resolution. Remote sensing data with the high spatial resolution is especially needed for urban monitoring. However, high spatial resolution data is usually expensive with limited coverage and complex analysis. This paper aims to find the most efficient way to do urban monitoring, specifically surface material identification. In material identification, the distinctive feature that can be used to differentiate one material surface from one another is its reflectance responses. This leads to a question of which absorption features are significant to different surface materials, especially roofing materials, and which absorption features are not discriminant enough to be used at classification. This paper proposed a machine learning-based identification of roof material types using band combinations as classification features. The experiment was done on Pleiades data, multispectral satellite imagery with very high spatial resolution. We first calculated the image’s reflectance values for each band and then grouped them based on their spectral range, yielding 11 possible combinations as the classification features. The experiment found that reflectance responses for band Red and NIR are the most distinctive trait of a material type and thus sufficient for material identification. We minimized the number of spectral responses used in material identification down to two bands, which can help the data collection and processing of material identification easier, cheaper, and less time-cost. Our experiment yields overall accuracy of 0.9959, with a computational time of 19.72 seconds.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=13102
DO - 10.18517/ijaseit.11.5.13102
