Abstract

Hyperspectral image (HSI), acquired in narrow and contiguous bands, contains redundant information in neighbouring bands. In order to overcome the processing overhead of this data like Hughes phenomenon, dimension of HSI is reduced either by using feature extraction or selection technique. Most of the existing dimensionality reduction methods depend on the user input to provide the reduced set of bands. However, perceiving the required number of bands in advance is difficult, since this number varies with dataset. In order to cope up with this limitation some wrapper-based methods are suggested which are again computationally inefficient. Hence, the present research focuses on a supervised data-driven approach that enables in selecting required number of bands without any user intervention. In proposed method, signature patterns with minimum and maximum reflectance values are extracted for each class from the labeled data, which are subsequently quantized. The quantization process continues till unique patterns are obtained for each class. Finally, bands having maximum correlation and minimum variance are eliminated to ensure minimum redundancy among selected bands. The experimental result shows that, an improved classification accuracy is obtained while using the proposed method on two real HSIs as compared to other state-of-the-art methods.

摘要

在窄而连续的波段中获取的高光谱图像 (HSI) 包含相邻波段中的冗余信息。为了克服像休斯现象这样的数据处理开销，通过使用特征提取或选择技术来降低 HSI 的维度。大多数现有的降维方法都依赖于用户输入来提供降维集。然而，提前感知所需的波段数量是困难的，因为这个数字随数据集而变化。为了克服这个限制，一些基于包装的方法被提出，这些方法在计算上仍然是低效的。因此，本研究侧重于一种有监督的数据驱动方法，该方法能够在没有任何用户干预的情况下选择所需数量的频段。在提出的方法中，从标记数据中为每个类别提取具有最小和最大反射率值的特征模式，然后对其进行量化。量化过程继续进行，直到为每个类别获得独特的模式。最后，消除具有最大相关性和最小方差的波段，以确保所选波段之间的冗余最小。实验结果表明，与其他最先进的方法相比，在两个真实的 HSI 上使用所提出的方法可以获得更高的分类精度。消除具有最大相关性和最小方差的波段，以确保所选波段之间的冗余最小。实验结果表明，与其他最先进的方法相比，在两个真实的 HSI 上使用所提出的方法可以获得更高的分类精度。消除具有最大相关性和最小方差的波段，以确保所选波段之间的冗余最小。实验结果表明，与其他最先进的方法相比，在两个真实的 HSI 上使用所提出的方法可以获得更高的分类精度。