Hyperspectral image classification with combination of weighted spatial-spectral and KNN

HUANG Hong; ZHENG Xin-lei

doi:10.3788/OPE.20162404.0873

您当前的位置：

首页 >

文章列表页 >

Hyperspectral image classification with combination of weighted spatial-spectral and KNN

更新时间：2020-08-13

- Hyperspectral image classification with combination of weighted spatial-spectral and KNN
- Optics and Precision Engineering Vol. 24, Issue 4, Pages: 873-881(2016)
- 作者机构：
  
  重庆大学光电技术与系统教育部重点实验室重庆,400044
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162404.0873
  CLC： TP751.1
- Received：15 December 2015，
  
  Revised：14 January 2016，
  
  Published：25 April 2016
- 稿件说明：
移动端阅览
黄鸿, 郑新磊,. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016,24(4): 873-881

HUANG Hong, ZHENG Xin-lei,. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 873-881
黄鸿, 郑新磊,. 加权空-谱与最近邻分类器相结合的高光谱图像分类[J]. 光学精密工程, 2016,24(4): 873-881 DOI： 10.3788/OPE.20162404.0873.

HUANG Hong, ZHENG Xin-lei,. Hyperspectral image classification with combination of weighted spatial-spectral and KNN[J]. Editorial Office of Optics and Precision Engineering, 2016,24(4): 873-881 DOI： 10.3788/OPE.20162404.0873.

摘要

提出了一种基于加权空-谱距离(WSSD)的相似性度量方法

并将其应用到最近邻分类器(KNN)中

导出了一种新的高光谱图像分类算法。该算法利用高光谱图像的物理特性

通过引入空间窗口和光谱因子这两个参数来挖掘出图像中的空间信息与光谱信息

利用空间近邻点对中心像元进行重构。在最大限度减少图像冗余信息的基础上

增大了同类像元间的相似性以及异类像元间的差异性

获得了更为有效的鉴别特征

从而更好地实现了数据间的相似性度量。基于Indian Pines和PaviaU高光谱数据集进行了实验

结果表明:将提出的WSSD-KNN算法应用于高光谱图像分类时

其分类精度高于其他算法

总体分类精度分别达到了91.72%和96.56%。由于算法较好地融合了图像中的空间-光谱信息

提取出了更为有效的鉴别特征

故不仅有效地改善了高光谱数据的地物分类精度

而且可在训练样本较少时

保持较高的识别率。

Abstract

A spatial consistency measurement method based on the Weighted Spatial-Spectral Distance(WSSD) is proposed and applied to the K Nearest Neighbor(KNN) classifier

and a new hyperspectral image classification algorithm is obtained. On the basis of the physical characters of hyperspectral images

the proposed algorithm combines both spatial window and spectral factor to obtain the spatial information and spectral information

and uses the spatial nearest points to reconstruct the center point and to reveal the local spatial structure. With effectively reducing the redundant information in the image

this algorithm increases the consistency of the same kinds pixels and the difference of the different kinds pixels and obtains extract discriminating features

so it implements the consistency measurement between the data points. The experiments were performed on the Indian Pines and PaviaU hyperspectral data sets. Experiment results show that the WSSD-KNN algorithm has better classification accuracy than other algorithms when it is applied to the classification of hyperspectral image

and the overall classification accuracies reach 91.72% and 96.56%

respectively. With the spectral information

spatial information and extract discriminating features

the proposed algorithm effectively improves ground object classification accuracy of hyperspectral data and has better recognition ability in less train samples.

关键词

Keywords

references

李志敏, 张杰, 黄鸿, 等. 面向高光谱图像分类的半监督丛流形学习[J]. 光学精密工程, 2015, 23(5):1434-1442. LI ZH M, ZHANG J, HUANG H, et al.. Semi-supervised bundle manifold learning for hyperspectral image classification[J]. Opt. Precision Eng., 2015, 23(5):1434-1442. (in Chinese)

张达, 郑玉权. 高光谱遥感的发展与应用[J]. 光学与光电技术, 2013, 11(3):67-73. ZHANG D, ZHENG Y Q. The development and application of hyperspectral remote sensing[J]. Optical and Photoelectric Technology, 2013, 11(3):67-73. (in Chinese)

唐中奇, 付光远, 陈进, 等. 基于多尺度分割的高光谱图像稀疏表示与分类[J]. 光学精密工程, 2015, 23(9):2708-2714. TANG ZH Q, FU G Y, CH J, et al.. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Opt. Precision Eng., 2015, 23(9):2708-2714. (in Chinese)

FAUVEL M, TARABALKA Y, BENEDIKTSSON J A, et al.. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE, 2013, 101(3):652-675.

黄鸿, 曲焕鹏. 基于半监督稀疏鉴别嵌入的高光谱遥感影像分类[J]. 光学精密工程, 2014, 22(2):434-442. HUANG H, QU H P. Hyperspectral remote sensing image classification based on SSDE[J]. Opt. Precision Eng., 2014, 22(2):434-442. (in Chinese)

MA L, CRAWFORD M M, TIAN J W. Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11):4099-4109.

SHAFRI H Z M, SUHAILI A, MANSOR S. The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis[J]. Journal of Computer Science, 2007, 3(6):419-423.

GUO B F, GUNN S R, DAMPER R I, et al.. Customizing kernel functions for SVM-based hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2008, 17(4):622-629.

郭敬明, 何昕, 魏仲慧. 基于在线支持向量机的Mean Shift彩色图像跟踪[J]. 液晶与显示, 2014, 29(1):120-128. GUO J M, HE X, WEI ZH H. New mean shift tracking for color image based on online support vector machine[J]. Chinese Journal of Liquid Crystals and Displays, 2014, 29(1):120-128. (in Chinese)

魏峰, 何明一, 梅少辉. 空间一致性邻域保留嵌入的高光谱数据特征提取[J]. 红外与激光工程, 2012, 41(5):1249-1254. WEI F, HE M Y, MEI SH H. Hyperspectral data feature extraction using spatial coherence based neighborhood preserving embedding[J]. Infrared and Laser Engineering, 2012, 41(5):1249-1254. (in Chinese)

VELASCO F S, MANIAN V. Improving hyperspectral image classification using spatial preprocessing[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2):297-301.

TAN K, HU J, LI J, et al.. A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105:19-29.

MOHAN A, SAPIRO G, BOSCH E. Spatially coherent nonlinear dimensionality reduction and segmentation of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(2):206-210.

PU H Y, CHEN Z, WANG B, et al.. A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11):7008-7022.

KANG X D, LI S T, BENEDIKTSSON J A. Spectral-Spatial hyperspectral image classification with Edge-Preserving Filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5):2666-2677.

YUAN H L, TANG Y Y, LU Y, et al.. Spectral-Spatial classification of hyperspectral image based on discriminant analysis[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2035-2043.

Views

380

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Dermoscopic image classification based on multi-scale and three-dimensional interaction feature optimization

Specral-spatial classification of hyperspectral imagery with hybrid architecture of 3D-CNN and Transformer

Collaborative classification of hyperspectral and LiDAR data based on CNN-transformer

Hyperspectral unmixing with shared endmember variability in homogeneous region

A lightweight deep learning model for TFT-LCD circuits defect classification based on swin transformer

Related Author

WANG Di

LÜ Xiaoqi

LI Jing

JING Haizhao

TAO Lijie

ZHANG Haokui

WU Haibin

DAI Shiyu

Related Institution

School of Information Engineering， Inner Mongolia University of Technology

School of Digital and Intelligence Industry， Inner Mongolia University of Science and Technology

Northwestern Polytechnical University

College of Electron and Information， University of Electronic Science and Technology of China，Zhongshan Institute

Department of Computer Science， Chubu University

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰