Remote Sensing Data Feature Analysis Using Spatial Linear Embedding (SLE)
- Lifang Xue, Xiushuang Yi, Xiumei Liu, Jie Li
- Corresponding Author
- Lifang Xue
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.17How to use a DOI?
- Spatial linear embedding; Remote sensing data feature analysis; Manifold learning.
- Dimensionality reduction has been used to reduce the complexity of the representation of remote sensing data. In this paper, a novel remote sensing data feature analysis method is proposed based on an improved manifold learning algorithm--spatial linear embedding. The purpose of feature extraction is to reduce the dimensionality of the remote sensing data while preserving the significant information. Compared with LLE, spatial linear embedding method emphasizes the relation of neighboring pixels spatially to increase system efficiency.The method makes up the shortage that LLE ignores the relation of neighboring pixels spatially which is extremely important for remote sensing data. In this paper we have obtain experiment results from the analysis of remote sensing data using PCA and spatial linear embedding. The results show that the SLEcan give significantly higher accuracies than the linear method of PCA.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lifang Xue AU - Xiushuang Yi AU - Xiumei Liu AU - Jie Li PY - 2015/12 DA - 2015/12 TI - Remote Sensing Data Feature Analysis Using Spatial Linear Embedding (SLE) BT - 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering PB - Atlantis Press UR - https://doi.org/10.2991/icmmcce-15.2015.17 DO - https://doi.org/10.2991/icmmcce-15.2015.17 ID - Xue2015/12 ER -