Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative dictionary by exploiting both spatial and spectral information all over the whole image. Rather than only using training samples for dictionary construction, the online dictionary learning (ODL) mechanism can effectively improve the adaptive representation capability of different pixels. Specifically, the contextual characteristics of HSI are integrated with discriminative spectral information for the ODL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients w.r.t. the well learnt dictionary. By this way, the yielding sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., linear support vector mechine (SVM), is applied to the sparse coefficients and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.
|Title of host publication||2017 IEEE International Geoscience and Remote Sensing Symposium|
|Subtitle of host publication||International Cooperation for Global Awareness, IGARSS 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|Publication status||Published - 1 Dec 2017|
|Event||37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States|
Duration: 23 Jul 2017 → 28 Jul 2017
|Name||International Geoscience and Remote Sensing Symposium (IGARSS)|
|Conference||37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017|
|Period||23/07/17 → 28/07/17|
Bibliographical noteFunding Information:
This paper is supported by the National Natural Science Fund of China for Distinguished Young Scholars (No. 61325007) and the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001).
© 2017 IEEE.
- Contextual characteristics
- Hyperspectral images
- Online dictionary learning