Abstract
Information extraction from spatial big data faces challenges in data relevancy analysis and heterogeneous data modeling. When the interested targets are more than one, the relevant analysis is often compromised. In this paper, a one-class oriented approach for effective feature selection and classification of remote sensing images is proposed. Mutual information (MI) is used as the feature selection criterion to cope with a wide range of data types. Then a cluster space (CS) representation is applied to model multimodal data and classifies each target class in turn. Hyperspectral and LiDAR data sets were used in the experiments. The test results demonstrate the advantage in terms of classification accuracies by focusing on one class at a time as compared to considering all classes simultaneously in classification.
| Original language | English |
|---|---|
| Article number | 7409958 |
| Pages (from-to) | 1606-1612 |
| Number of pages | 7 |
| Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Volume | 9 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Apr 2016 |
Bibliographical note
Funding Information: The AVIRIS Indian Pine data were provided by Prof. D. A. Landgrebe, Purdue University, W. Lafayette, IN, USA. The AISA/LiDAR Trento data were provided by Prof. L. Bruzzone, University of Trento, Trento, Italy. Publisher Copyright: © 2016 IEEE.Other keywords
- Cluster space (CS) classification
- feature extraction
- feature selection
- image classification
- mutual information (MI)
- remote sensing