Long time series urban mapping using Landsat images remains challenging due to the difficulty of collecting representative ground samples. We proposed a mapping approach integrating a sample-transferring scheme, multiple-feature fusion classification, and temporal filtering to generate annual urban land maps in Zhengzhou during 1986 to 2021 with the aid of the Google Earth Engine (GEE). The training and validation samples for each historical year were obtained by a proposed sample transfer scheme based on stable areas derived from the GlobeLand30 datasets. Thirteen variables, such as spectral indices, spectral bands, and terrain factors, were combined to form the feature vector. Annual classification was conducted yearly using the random forest algorithm on GEE to obtain annual impervious surface (ISs) maps. Finally, the IS classification maps were postprocessed to obtain the final urban land maps. Zhengzhou was selected as the study area due to its dramatic urbanization in recent decades. The results showed that the overall accuracy and Kappa coefficient of the IS classification maps ranged from 0.8888 to 0.9578 and 0.6548 to 0.8331, respectively. Compared with other 30-m land-cover products, our urban land maps showed higher accuracy and more reliable spatial details. The proposed approach can generate massive ground samples for long time series urban mapping and is helpful for updating regional and global land-cover products. Zhengzhou’s urban area increased by 1423.24 km2 with an average expansion area of 40.66 km2 / year from 1986 to 2021. The urban expansion in Zhengzhou showed significant stage characteristics and spatial variations. The expansion intensity indices were 0.204, 0.404, 0.736, and 2.018, respectively, at four time periods and varied across the 12 regions in Zhengzhou.
This paper proposes a robust feature matching for slant SAR images based on differentially constrained RANSAC. Firstly, we obtain a set of initial matches provided by SAR-SIFT operator. Then, we set a differentially constrained model with strong constraint in the azimuth direction and no constraint in the distance direction. We use the nearest neighbor contrast (NNC) and the distance offset definition (DOD) techniques to eliminate outliers with large distance distortion. Experiments are carried out on Chinese spaceborne and airborne SAR images. The results show that the proposed method has excellent performance in accuracy, distribution and efficiency, and is suitable for matching SAR images covering areas with large terrain fluctuations such as mountainous areas.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.