Feature Level Fusion for Hyperspectral Images

机译：高光谱图像的特征级融合

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This paper presents a new method for detecting poultry skin tumors based on serial feature fusion in hyperspectral images. First, some transform methods, including principal component analysis, discrete wavelet transform and band ratio method, are used to generate largely independent datasets in the hyperspectral fluorescence images. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset for the purpose of classification; another set of features are extracted from hyperspectral reflectance images by using kernel discriminant analysis. Finally, new fused features are made by combining aforementioned features. The experimental result based on the proposed method shows the better performance in detecting tumors compared with previous works.

机译：本文提出了一种基于高光谱图像序列特征融合的禽类皮肤肿瘤检测新方法。首先，一些变换方法，包括主成分分析，离散小波变换和带比方法，被用于在高光谱荧光图像中生成很大程度上独立的数据集。然后，利用内核判别分析从每个表示的数据集中提取特征，以进行分类。使用核判别分析从高光谱反射率图像中提取另一组特征。最后，通过组合前述特征来制造新的融合特征。基于该方法的实验结果表明，与以前的工作相比，该方法在检测肿瘤方面具有更好的性能。

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来源
《Sensing for agriculture and food quality and safety》|2009年|73150N.1-73150N.6|共6页
会议地点 Orlando FL(US)
作者
Chengzhe Xu; Intaek Kim; Seong G. Kong;
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作者单位

Dept. of Communication Eng., Myongji University, 38-2 Namdong, Yongin, Korea;

Dept. of Communication Eng., Myongji University, 38-2 Namdong, Yongin, Korea;

Dept. of Electrical and Computer Engineering, Temple University, 1947 North 12st St.,Philadelphia, PA 19122, USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
hyperspectral image; feature fusion; feature extraction; fisher ratio;

机译：高光谱图像特征融合特征提取;费舍尔比率;

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1. A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data [J] . Hadiseh Hasani, Peter Reinartz, Farhad Samadzadegan European Journal of Remote Sensing . 2017,第1期

机译：利用高光谱图像和LiDAR数据进行城市分类的元启发式特征级融合策略
2. A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data [J] . Hadiseh Hasani, Peter Reinartz, Farhad Samadzadegan European Journal of Remote Sensing . 2017,第1期

机译：利用高光谱图像和LiDAR数据进行城市分类的元启发式特征级融合策略
3. Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering [J] . IEEE Transactions on Geoscience and Remote Sensing . 2014,第6期

机译：基于图像融合和递归滤波的高光谱图像特征提取
4. Feature Level Fusion for Hyperspectral Images [C] . Chengzhe Xu, Intaek Kim, Seong G. Kong SPIE Conference on Sensing for Agriculture and Food Quality and Safety . 2009

机译：高光谱图像的特征级融合
5. Development of the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT) with a focus on hyperspectral and lidar data fusion. [D] . Torres Madronero, Maria Constanza. 2009

机译：开发了高光谱海岸图像分析工具箱（HyCIAT），重点是高光谱和激光雷达数据融合。
6. Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery [O] . Baohua Yang, Jifeng Ma, Xia Yao, 2021

机译：基于近红外高光谱图像的光谱特征融合和深度特征的小麦叶片氮含量估算
7. A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data [O] . Hasani, Hadiseh, Samadzadegan, Farhad, Reinartz, Peter 2017

机译：利用高光谱图像和LiDAR数据进行城市分类的元启发式特征级融合策略
8. Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain [R] . Mendenhall, M. J. 2006

机译：高光谱图像特征提取的神经关联模型及其在小波域中的应用

Feature Level Fusion for Hyperspectral Images

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