Land cover classification and change detection analysis of multispectral satellite images using machine learning

Nyein Soe Thwal; Takaaki Ishikawa; Hiroshi Watanabe

doi:10.1117/12.2532988

7 October 2019 Land cover classification and change detection analysis of multispectral satellite images using machine learning

Nyein Soe Thwal, Takaaki Ishikawa, Hiroshi Watanabe

Author Affiliations +

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 111551O (2019) https://doi.org/10.1117/12.2532988
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

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.

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Nyein Soe Thwal, Takaaki Ishikawa, and Hiroshi Watanabe "Land cover classification and change detection analysis of multispectral satellite images using machine learning", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551O (7 October 2019); https://doi.org/10.1117/12.2532988

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KEYWORDS

Earth observing sensors

Satellites

Landsat

Satellite imaging

Image classification

Image segmentation

Feature extraction

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