Feature Extraction in Remote Sensing High-Dimensional Image Data

Zortea M.; Haertel V.; Clarke R.

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Feature Extraction in Remote Sensing High-Dimensional Image Data

机译：遥感高维图像数据中的特征提取

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High-dimensional image data open new possibilities in remote sensing digital image classification, particularly when dealing with classes that are spectrally very similar. The main problem refers to the estimation of a large number of classifier's parameters. One possible solution to this problem consists in reducing the dimensionality of the original data without a significant loss of information. In this letter, a new approach to reduce data dimensionality is proposed. In the proposed methodology, each pixel's curve of spectral response is initially segmented, and the digital numbers (DNs) at each segment are replaced by a smaller number of statistics. In this letter, the proposed statistics are the mean and variance of the segment's DNs, which are supposed to carry information about the segment's position and shape, respectively. Tests were performed by using Airborne Visible/Infrared Imaging Spectrometer hyperspectral image data. The experiments have shown that this methodology is capable of providing very acceptable results, in addition of being computationally efficient

机译：高维图像数据为遥感数字图像分类开辟了新的可能性，尤其是在处理光谱非常相似的类时。主要问题涉及大量分类器参数的估计。解决该问题的一种可能的方法是在不损失大量信息的情况下减小原始数据的维数。在这封信中，提出了一种减少数据维数的新方法。在所提出的方法中，首先将每个像素的光谱响应曲线进行分段，然后将每个分段处的数字（DN）替换为较少数量的统计数据。在这封信中，建议的统计数据是段DN的均值和方差，假定这些DN分别携带有关段的位置和形状的信息。通过使用机载可见/红外成像光谱仪高光谱图像数据进行测试。实验表明，该方法除具有计算效率外，还能够提供非常令人满意的结果

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《IEEE Geoscience and Remote Sensing Letters》 |2007年第2007期|p.107-111|共5页
作者
Zortea M.; Haertel V.; Clarke R.;
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正文语种 eng
中图分类测绘遥感技术;
关键词
data reduction; feature extraction; geophysical techniques; geophysics computing; remote sensing; Airborne Visible/ Infrared Imaging Spectrometer hyperspectral image; data dimensionality reduction; digital image classification; high-dimensional image; pixel spect;

机译：数据缩减;特征提取;地球物理技术;地球物理计算;遥感;机载可见/红外成像光谱仪高光谱图像;数据降维;数字图像分类;高维图像;像素点;

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1. A fast separability-based feature-selection method for high-dimensional remotely sensed image classification [J] . Guo BF, Damper RI, Gunn SR, Pattern Recognition: The Journal of the Pattern Recognition Society . 2008,第5期

机译：基于快速可分离性的高维遥感图像分类特征选择方法
2. Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging [Applications Corner] [J] . Ren J., Zabalza J., Marshall S., Signal Processing Magazine, IEEE . 2014,第4期

机译：使用高光谱成像进行遥感中有效的特征提取和数据缩减[应用角落]
3. Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [J] . Jaime Zabalza, Jinchang Ren, Mingqiang Yang, ISPRS Journal of Photogrammetry and Remote Sensing . 2014,第jula期

机译：新型Folded-PCA，可通过高光谱成像和SAR改进遥感中的特征提取和数据缩减
4. Human-experts rules modeling for linear planimetric features extraction in a remotely sensed images data fusion context [C] . Pigeon, L., Solaiman, Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International . 1999

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5. Knowledge-based feature extraction and spectral image enhancement from remotely sensed images. [D] . Sun, Weihua. 2013

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机译：中性网络融合架构从卫星遥感影像中自动提取海洋特征

Feature Extraction in Remote Sensing High-Dimensional Image Data

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