• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification
【24h】

Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification

机译：空间光谱高光谱图像分类的概率核协同表示

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a new approach for accurate spatial–spectral classification of hyperspectral images, which consists of three main steps. First, a pixelwise classifier, i.e., the probabilistic-kernel collaborative representation classification (PKCRC), is proposed to obtain a set of classification probability maps using the spectral information contained in the original data. This is achieved by means of a kernel extension based on collaborative representation (CR) classification. Then, an adaptive weighted graph (AWG)-based postprocessing model is utilized to include the spatial information by refining the obtained pixelwise probability maps. Furthermore, to deal with scenarios dominated by limited training samples, we modify the postprocessing model by fixing the probabilistic outputs of training samples to integrate the spatial and label information. The proposed approach is able to cover different analysis scenarios by means of a fully adaptive processing chain (based on three steps) for hyperspectral image classification. All the techniques that integrate the proposed approach have a closed-form analytic solution and are easy to be implemented and calculated, exhibiting potential benefits for hyperspectral image classification under different conditions. Specifically, the proposed method is experimentally evaluated using two real hyperspectral imagery data sets, exhibiting good classification performance even when the number of training samples available is very limited.
机译：本文提出了一种用于高光谱图像的准确空间光谱分类的新方法，该方法包括三个主要步骤。首先，提出了像素级分类器，即概率核协同表示分类（PKCRC），以使用原始数据中包含的频谱信息来获得一组分类概率图。这是通过基于协作表示（CR）分类的内核扩展来实现的。然后，基于自适应加权图（AWG）的后处理模型可用于通过细化所获得的逐像素概率图来包含空间信息。此外，为了处理由有限训练样本主导的场景，我们通过固定训练样本的概率输出以整合空间和标签信息来修改后处理模型。所提出的方法能够通过针对高光谱图像分类的完全自适应处理链（基于三个步骤）来涵盖不同的分析场景。集成了所提出方法的所有技术都具有封闭形式的解析解决方案，并且易于实现和计算，在不同条件下展现出对高光谱图像分类的潜在好处。具体来说，使用两个真实的高光谱图像数据集对所提出的方法进行了实验评估，即使在可用的训练样本数量非常有限的情况下，也表现出良好的分类性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号