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arXiv 1906.06804cs.CV
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三维傅里叶散射变换与高光谱图像分类

Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

UMIACS, University of Maryland at College Park（马里兰大学帕克分校高级计算机研究所）；University of Maryland at College Park（马里兰大学帕克分校）；Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所）

Ilya Kavalerov, Weilin Li, Wojciech Czaja, Rama Chellappa

备注：
Accepted to IEEE Transactions On Geoscience And Remote Sensing
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摘要：

机器学习和信号处理的最新发展产生了许多新技术，这些技术能够有效地捕获高光谱图像的内在而复杂的特性。从异常检测到分类的任务现在可以通过利用非常有效的算法来解决，这些算法的根源在于表示理论和计算近似。时间-频率方法是这种技术的一个示例。它们提供了从数据中分析和提取光谱内容的方法。另一方面，诸如神经网络的分层方法结合了跨尺度的空间信息，并对光谱特征之间的多个依赖性级别进行建模。这两种方法最近已被证明是提供显着的进步，在高光谱图像的光谱空间分类。本文介绍的三维傅里叶散射变换是时频表示与神经网络结构的融合。它利用短时傅里叶变换提供的优势和深度学习网络结构的数值效率。我们测试所提出的方法在几个标准的高光谱数据集，我们目前的结果表明，3D傅立叶散射变换是非常有效的表示光谱内容相比，与其他国家的最先进的光谱空间分类方法。

英文摘要：

Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery. Tasks ranging from anomaly detection to classification can now be solved by taking advantage of very efficient algorithms which have their roots in representation theory and in computational approximation. Time-frequency methods are one example of such techniques. They provide means to analyze and extract the spectral content from data. On the other hand, hierarchical methods such as neural networks incorporate spatial information across scales and model multiple levels of dependencies between spectral features. Both of these approaches have recently been proven to provide significant advances in the spectral-spatial classification of hyperspectral imagery. The 3D Fourier scattering transform, which is introduced in this paper, is an amalgamation of time-frequency representations with neural network architectures. It leverages the benefits provided by the Short-Time Fourier Transform with the numerical efficiency of deep learning network structures. We test the proposed method on several standard hyperspectral datasets, and we present results that indicate that the 3D Fourier scattering transform is highly effective at representing spectral content when compared with other state-of-the-art spectral-spatial classification methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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