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首页> 外文会议>Conference on Image and Signal Processing for Remote Sensing VIII, Sep 24-27, 2002, Agia Pelagia, Crete, Greece >A comparison of feature reduction techniques for classification of hyperspectral remote-sensing data
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A comparison of feature reduction techniques for classification of hyperspectral remote-sensing data

机译：特征缩减技术在高光谱遥感数据分类中的比较

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摘要

The task of the analysis of hyperspectral data, due to their high spectral resolution, requires dealing with the problem of the curse of dimensionality. Many feature selection/extraction techniques have been developed, which map the hyperdimensional feature space in a lower-dimensional space, based on the optimization of a suitable criterion function. This paper studies the impact of several such techniques and of the criterion chosen on the accuracy of different supervised classifiers (the "minimal-distance-to-means", the k-NN, and the Bayes classifier with Gaussian distributions). The compared methods are the "Sequential Forward Selection" (SFS), the "Steepest Ascent" (SA), the "Fast Constrained Search" (FCS), the "Projection Pursuit" (PP) and the "Decision Boundary Feature Extraction" (DBFE), while the considered criterion functions are standard interclass distance measures (Bhattacharyya, Jeffries-Matusita and divergence distances). SFS is well known for its conceptual and computational simplicity. SA provides more effective subsets of selected features at the price of a higher computational cost. DBFE is an effective transformation technique, usually applied after a preliminary feature-space reduction through PP. The experimental comparison is performed on an AVIRIS hyperspectral data set characterized by 220 spectral bands and nine ground cover classes. The computational time of each algorithm is also reported.
机译：由于高光谱数据的高光谱分辨率，分析高光谱数据的任务需要处理维数诅咒问题。已经开发了许多特征选择/提取技术，其基于合适的准则函数的优化在低维空间中映射超维特征空间。本文研究了几种此类技术和所选标准对不同监督分类器（“均值最小距离”，k-NN和具有高斯分布的贝叶斯分类器）的准确性的影响。比较的方法是“顺序向前选择”（SFS），“最陡峭上升”（SA），“快速约束搜索”（FCS），“投影追踪”（PP）和“决策边界特征提取”（ DBFE），而考虑的标准函数是标准的类间距离度量（Bhattacharyya，Jeffries-Matusita和散度距离）。 SFS以其概念和计算简单性而闻名。 SA以更高的计算成本为代价提供了选定特征的更有效子集。 DBFE是一种有效的转换技术，通常在通过PP进行初步的特征空间缩减之后应用。实验比较是在以220个光谱带和9个地面覆盖类别为特征的AVIRIS高光谱数据集上进行的。还报告了每种算法的计算时间。

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