Extinction Profiles for the Classification of Remote Sensing Data
With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatia...
| Published in: | IEEE Transactions on Geoscience and Remote Sensing |
|---|---|
| Main Authors: | , , , , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
IEEE - Institute of Electrical and Electronics Engineers
2016
|
| Subjects: | |
| Online Access: | https://elib.dlr.de/103092/ https://elib.dlr.de/103092/1/07514921.pdf http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7514921 |
| Summary: | With respect to recent advances in remote sensing technologies, the spatial resolution of airborne and spaceborne sensors is getting finer, which enables us to precisely analyze even small objects on the Earth. This fact has made the research area of developing efficient approaches to extract spatial and contextual information highly active. Among the existing approaches, morphological and attribute profiles have gained great attention due to their ability to classify remote sensing data. This paper proposes a novel approach that makes it possible to precisely extract spatial and contextual information from remote sensing images. The proposed approach is based on extinction filters, which are used here for the first time in the remote sensing community. Then, the approach is carried out on two well-known high resolution panchromatic data sets captured over Rome, Italy, and Reykjavik, Iceland. In order to prove the capabilities of the proposed approach, the obtained results are compared with results from one of the strongest approaches in the literature, attribute profiles, using different points of view such as classification accuaracies, simplification rate, and complexity analysis. Results indicate that the proposed approach can significantly outperform its alternative in terms of classification accuracies. In addition, based on our implementation, profiles can be generated in a very short processing time. It should be noted that the proposed approach is fully automatic. |
|---|