TY - GEN
T1 - Land cover classification and change detection analysis of multispectral satellite images using machine learning
AU - Soe Thwal, Nyein
AU - Ishikawa, Takaaki
AU - Watanabe, Hiroshi
PY - 2019
Y1 - 2019
N2 - Land cover classification and change detection analysis based on remote sensing images using machine learning algorithm has become one of the important factors for environmental management and urban planning. We select Yangon as the study area because the government faces many problems in urban planning sectors due to the population growth and urban sprawl. Therefore, the proposed method aims to perform the land cover classification in Yangon using Random Forest (RF) classifier in Google Earth Engine (GEE) and post-classification change detection between 1987 and 2017 with 5 years interval periods are evaluated. Despite land cover classifications using satellite imagery have been executed in the past decades, the classification of remotely sensed data integrating with multiple spectral, temporal and textural features and processing time for classification using time series data still have limitations. To overcome these limitations, features extracted from Sentinel-2, Landsat-8, Landsat-7, Landsat-5 and Open Street Map (OSM) are executed for classification and cloud-based GEE platform is used to reduce the processing time. Some spectral indexes such as NDVI, NDBI and slope from SRTM are calculated to achieve better classification. Land cover classification is performed by using the RF classifier with the different bands' combination. Land cover classification map with 7 classes (Shrub Land, Bare Land, Forest, Vegetation, Urban Area, Lake and River) is obtained with the overall accuracy of 96.73% and kappa statistic of 0.95 for 2017. Finally, change detection analysis over 30 years is performed and the significant changes in build-up, bare land, and agriculture have been resulted.
AB - Land cover classification and change detection analysis based on remote sensing images using machine learning algorithm has become one of the important factors for environmental management and urban planning. We select Yangon as the study area because the government faces many problems in urban planning sectors due to the population growth and urban sprawl. Therefore, the proposed method aims to perform the land cover classification in Yangon using Random Forest (RF) classifier in Google Earth Engine (GEE) and post-classification change detection between 1987 and 2017 with 5 years interval periods are evaluated. Despite land cover classifications using satellite imagery have been executed in the past decades, the classification of remotely sensed data integrating with multiple spectral, temporal and textural features and processing time for classification using time series data still have limitations. To overcome these limitations, features extracted from Sentinel-2, Landsat-8, Landsat-7, Landsat-5 and Open Street Map (OSM) are executed for classification and cloud-based GEE platform is used to reduce the processing time. Some spectral indexes such as NDVI, NDBI and slope from SRTM are calculated to achieve better classification. Land cover classification is performed by using the RF classifier with the different bands' combination. Land cover classification map with 7 classes (Shrub Land, Bare Land, Forest, Vegetation, Urban Area, Lake and River) is obtained with the overall accuracy of 96.73% and kappa statistic of 0.95 for 2017. Finally, change detection analysis over 30 years is performed and the significant changes in build-up, bare land, and agriculture have been resulted.
KW - Change detection
KW - Classification
KW - Google Earth Engine
KW - Land cover
KW - Machine learning
KW - Random forest
UR - http://www.scopus.com/inward/record.url?scp=85078161193&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078161193&partnerID=8YFLogxK
U2 - 10.1117/12.2532988
DO - 10.1117/12.2532988
M3 - Conference contribution
AN - SCOPUS:85078161193
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Image and Signal Processing for Remote Sensing XXV
A2 - Bruzzone, Lorenzo
A2 - Bovolo, Francesca
A2 - Benediktsson, Jon Atli
PB - SPIE
T2 - Image and Signal Processing for Remote Sensing XXV 2019
Y2 - 9 September 2019 through 11 September 2019
ER -