Name
Title | ||
|---|---|---|
Mapping The Land Development Processes Using Data Transformation And Clustering Methods |
Abstract | ||
|---|---|---|
Understanding the historical trend of land development provides an invaluable source of information for policy-makers, environmental planners, and regional scientists. This information is useful in the study of the impacts of past decisions and natural conditions on the patterns of the trend. In this study, we built a hybrid algorithm that benefits from various techniques of land change detection to map the land development process in a given region. Data transformation and band differencing were used to construct a new feature class from the temporal satellite data. For data of three consecutive decades, a pairwise comparison of images was conducted. A clustering algorithm was applied to the constructed feature class to identify the location of new land developments. The results indicate a user 's and producer 's accuracy of 90.3%. The findings of this study are useful in the study of historic trends in land development and its patterns. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IGARSS39084.2020.9323510 | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | DocType | Citations |
Principal Component Analysis (PCA), Band Differencing, Unsupervised Machine Learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pariya Pourmohammadi | 1 | 0 | 0.68 |
| Donald A. Adjeroh | 2 | 0 | 0.34 |
| Michael P. Strager | 3 | 0 | 0.34 |