Storing a collection of polygons using quadtrees
ACM Transactions on Graphics (TOG)
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Orthogonally persistent object systems
The VLDB Journal — The International Journal on Very Large Data Bases - Persistent object systems
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Safe query objects: statically typed objects as remotely executable queries
Proceedings of the 27th international conference on Software engineering
Remote sensing image information mining with HPC cluster and DryadLINQ
Proceedings of the 49th Annual Southeast Regional Conference
| Hi-index | 0.00 |
Classification and change detection of land cover types in the remotely sensed images is one of the major applications in remote sensing. This paper presents a hierarchical framework for land cover information storage and retrieval from object-oriented (OO) remote sensing image databases. Multi-spectral (band) remotely sensed images are classified by an optimized k-means clustering algorithm. The land cover maps are then decomposed and indexed with region quad-tree data structure stored in an OO database. Native queries (NQs), which use the semantics of the OO programming language for query composition, are developed to retrieve land cover distribution information and detect the changes of each land cover type at multi levels. A prototype system was implemented and the experiments were conducted on a time series Landsat Thematic Mapper (TM) images. The results show the effectiveness of the framework and the potentials in other remote sensing applications like urban planning and drought monitoring.