利用区域增长技术的自适应高光谱图像分类

吴银花; 高晓惠; 周安安

doi:10.3788/OPE.20182602.0426

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利用区域增长技术的自适应高光谱图像分类

信息科学 | 更新时间：2020-07-05

- 利用区域增长技术的自适应高光谱图像分类
- Adaptive hyperspectral image classification using region-growing techniques
- 光学精密工程 2018年26卷第2期页码：426-434
- 作者机构：
  
  中国科学院西安光学精密机械研究所, 陕西西安 710119
- 作者简介：
  
  [ "吴银花(1984-), 女, 吉林延吉人, 博士后, 2007年于吉林大学获得学士学位, 2012年于中国科学院大学院获得博士学位, 主要从事高光谱数据处理和高光谱成像技术方面的研究。E-mail:yinhuawoo@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(11327303);国家国际科技合作专项资助项目(2015DFA10140);国家自然科学基金资助项目(61405239)
- DOI：10.3788/OPE.20182602.0426
  中图分类号： TP751.1
- 收稿日期：2017-05-27，
  
  录用日期：2017-7-21，
  
  纸质出版日期：2018-02-25
- 稿件说明：
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吴银花, 高晓惠, 周安安. 利用区域增长技术的自适应高光谱图像分类[J]. 光学精密工程, 2018,26(2):426-434.

Yin-hua WU, Xiao-hui GAO, An-an ZHOU. Adaptive hyperspectral image classification using region-growing techniques[J]. Optics and precision engineering, 2018, 26(2): 426-434.
吴银花, 高晓惠, 周安安. 利用区域增长技术的自适应高光谱图像分类[J]. 光学精密工程, 2018,26(2):426-434. DOI： 10.3788/OPE.20182602.0426.

Yin-hua WU, Xiao-hui GAO, An-an ZHOU. Adaptive hyperspectral image classification using region-growing techniques[J]. Optics and precision engineering, 2018, 26(2): 426-434. DOI： 10.3788/OPE.20182602.0426.

摘要

针对面向对象的高光谱分类方法中分割参数设置问题，提出了一种基于区域增长技术的自适应高光谱分类算法。首先提出了带约束的区域增长方法，利用已知训练样本的空间信息，提供有效约束，从而降低区域增长过程中区域标记的错误传播率，以提高分类性能；其次，提出了自适应阈值计算方法，通过分析已知训练样本光谱的分布规律，自适应地计算出合理的区域划分阈值，从而代替经验阈值，提高算法的鲁棒性；最后，采用K近邻算法（KNN），对划分后各区域中心进行分类。实验结果表明：对于不同图像，提出的算法计算出的自适应阈值均与其经验值相符合，且其分类效果优于其他算法，来自AVIRIS传感器的高光谱数据Indian Pines在10%的已知训练样本下总体分类精度达92.94%、kappa系数达0.919 5，来自ROSIS传感器的高光谱数据Pavia University在5%的已知训练样本下总体分类精度达95.78%、kappa系数达0.944 0。该算法不仅增强了算法的鲁棒性，同时有效提高了分类性能，在高光谱应用中具有较强的实用性。

Abstract

Aiming at the problem of segmentation parameters setting in object-oriented hyperspectral classification method

an adaptive hyperspectral classification algorithm based on region-growing techniques was proposed in this paper. Firstly

a constrained region-growing method was proposed

which used the spatial information of the training samples to provide effective constraints

thus reducing the error propagation rate of the region markers in the region-growing process

and improving classification performance. Secondly

an adaptive threshold calculation method was proposed. By analyzing the distribution law of the spectrum of the training samples

the reasonable threshold for region division was calculated adaptively to replace the empirical threshold

so that the robustness of the algorithm was improved. Finally

the K-nearest neighbor algorithm (KNN) was used to classify the centers of each region after division. Experimental results show that:For different images

the adaptive thresholds calculated by the method are consistent with the empirical values

and the classification effect of the proposed algorithm is better than other algorithms. For hyperspectral data Indian Pines from AVIRIS sensor

the overall classification accuracy and kappa are 92.94% and 0.919 5 respectively with 10% training samples

and for hyperspectral data Pavia University from ROSIS sensor

the overall classification accuracy and kappa are 95.78% and 0.944 0 respectively with 5% training samples. The proposed algorithm not only enhances the robustness of the algorithm

but also improves the classification performance effectively

and has strong practicability in hyperspectral applications.

关键词

Keywords

references

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