Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images With Different Spatial Resolutions
The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.
A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images With Different Spatial Resolutions
The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a tradeoff between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse-spatial-resolution images have high temporal repetition rates. A novel superresolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and superresolution land-cover mapping. Both the land-cover change/no-change map and from-to change map at fine spatial resolution can be generated by SRCD. In this paper, SRCD was applied to a synthetic multispectral image, a Moderate-Resolution Imaging Spectroradiometer multispectral image, and a Landsat-8 Operational Land Imager multispectral image. The land-cover from-to change maps are found to have the highest overall accuracy (higher than 85%) in all of the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected.
A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images With Different Spatial Resolutions
Li, Xiaodong (Autor:in) / Ling, Feng / Foody, Giles M / Du, Yun
2016
Aufsatz (Zeitschrift)
Englisch
Lokalklassifikation TIB:
770/3710/5670
BKL:
38.03
Methoden und Techniken der Geowissenschaften
/
74.41
Luftaufnahmen, Photogrammetrie
Superresolution land cover mapping using spatial regularization
| Online Contents | 2014
Superresolution Land Cover Mapping Using Spatial Regularization
| Online Contents | 2014
Urban Change Detection and Population Prediction Modeling Using Remotely Sensed Images
| Online Contents | 2005
Monitoring Land Use/Cover Change Using Remotely Sensed Data in Guangzhou of China
| DOAJ | 2021
Land-cover Classification of Remotely Sensed Data Using Kalman Filtering
| British Library Online Contents | 1997