Knowledge Resource Center for Ecological Environment in Arid Area
| 项目编号 | 61461002 |
| 基于字典学习的小样本高光谱遥感图像稀疏表示分类精度研究与应用 | |
| 张春梅 | |
| 主持机构 | 北方民族大学 |
| 开始日期 | 2015 |
| 结束日期 | 2018 |
| 资助经费 | 400000(CNY) |
| 项目类别 | 地区科学基金项目 |
| 资助机构 | CN-NSFC（国家自然科学基金） |
| 语种 | 中文 |
| 国家 | 中国 |
| 英文简介 | The hyperspectral data classification of depopulated zones like desert and gobi is a challenge with small labelling samples because of acquiring samples difficultly and expensively in most situations. So, this study will try to improve the accuracy of hyperspectral classification with small samples by sparse representation based on dictionary learning. Sparse representations are able to extract the nature features of signals with redundant dictionaries and have a great potential in statistic pattern recognition. Sparse representation with dictionary learning can build an initial dictionary with a few training samples to solve the problems of insufficient labelling samples by constantly updating dictionaries. The research contents include：(1) To study the algorithm of dictionary update to improve its efficiency and convergence. (2) To study the functional relationship between classification accuracy and the amount of training samples, as well as the convergence and affecting factors of this funtional curve. (3) To constructing the feature models with multi-spectral features, as spectrum, vegetation and textures. To study the quality of data acquisition for labelling samples and improve classification accuracy in the depopulated zones. With this fundamental application research, classification application of sparse representation can be made progress toward engineering area and provide the theory and technical supports for hyperspectral data classifacation in the depopulated zones in Ningxia province. |
| 来源机构 | 北方民族大学 |
| 资源类型 | 项目 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/344377 |
| 推荐引用方式 GB/T 7714 | 张春梅.基于字典学习的小样本高光谱遥感图像稀疏表示分类精度研究与应用.2015. |
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