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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification
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Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification

机译：粗糙小波特征空间，深度自动码器和高光谱图像分类

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摘要

Prime objective of this letter is to select the most relevant features from the original set and perform two steps of feature extraction operations on those selected set, for the classification of hyperspectral remote sensing (HSRS) images. Neighborhood rough sets (NRSs)-based method is used for feature selection because of its excellent neighboring information capturing ability. On these selected features, two steps of extraction operations are performed using the stationary wavelet transform (WT) and stacked deep autoencoder (SDAE). Stationary WT extracts the features by exploiting the spectral-spatial information and stacked DAE extracts through representative learning of input information. The wavelet features and the original input spectral features are cascaded to feed as input to the stacks DAE for feature extraction and classification tasks. The proposed classification model with these operational steps possesses the ability to capture more informative features with improved spectral-spatial information that are highly beneficial for the classification of complex data sets, like HSRS images. Simulation results with two HSRS images justified the efficacy of the proposed model compared to other similar methods in terms of different performance measurement indexes.
机译：此字母的主要目标是从原始集合中选择最相关的功能，并在所选集合上执行两个特征提取操作的步骤，用于高光谱遥感（HSRS）图像的分类。基于邻域粗糙集（NRS）的方法用于特征选择，因为其相邻信息捕获能力优异。在这些所选特征上，使用静止小波变换（WT）和堆叠的深度AutoEncoder（SDAE）执行两步的提取操作。静止WT通过利用光谱空间信息和堆叠的DAE提取物通过代表性学习来提取特征。小波特征和原始输入光谱特征级联以作为特征提取和分类任务的堆栈DAE的输入来馈送。具有这些操作步骤的所提出的分类模型具有捕获更多信息特征的能力，具有改进的频谱空间信息，该信息对于复杂数据集的分类非常有利于HSRS图像。仿真结果具有两个HSRS在不同的性能测量指标方面与其他类似方法相比，所提出的模型的效果。

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