Djiongo Kenfack Cedrigue Boris, Monga Olivier, Mpong Serge Moto, Ndoundam René
UMMISCO, SAM Team, University of Yaoundé 1, P.O. Box 812, Yaoundé, Cameroon.
UMI 209, UMMISCO, Sorbonne Université, Univ. Paris 06, 75005, Paris, France.
Acta Biotheor. 2018 Mar;66(1):17-60. doi: 10.1007/s10441-018-9317-z. Epub 2018 Mar 21.
Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as color images. For practical application, multispectral satellite data appear as a primary source for measuring past trends and monitoring changes in forest carbon stocks. In this work, we propose a texture-color descriptor based on the quaternion Fourier transform to extract relevant information from multiband satellite images. We propose a new multiband image texture model extraction, called FOTO++, in order to address biomass estimation issues. The first stage consists in removing noise from the multispectral data while preserving the edges of canopies. Afterward, color texture descriptors are extracted thanks to a discrete form of the quaternion Fourier transform, and finally the support vector regression method is used to deduce biomass estimation from texture indices. Our texture features are modeled using a vector composed with the radial spectrum coming from the amplitude of the quaternion Fourier transform. We conduct several experiments in order to study the sensitivity of our model to acquisition parameters. We also assess its performance both on synthetic images and on real multispectral images of Cameroonian forest. The results show that our model is more robust to acquisition parameters than the classical Fourier Texture Ordination model (FOTO). Our scheme is also more accurate for aboveground biomass estimation. We stress that a similar methodology could be implemented using quaternion wavelets. These results highlight the potential of the quaternion-based approach to study multispectral satellite images.
在过去十年中，人们引入了几种使用四元数来整体处理和建模多波段图像的方法。四元数傅里叶变换可有效地用于对多维数据（如彩色图像）中的纹理进行建模。在实际应用中，多光谱卫星数据是测量过去趋势和监测森林碳储量变化的主要数据源。在这项工作中，我们提出了一种基于四元数傅里叶变换的纹理 - 颜色描述符，用于从多波段卫星图像中提取相关信息。为了解决生物量估计问题，我们提出了一种新的多波段图像纹理模型提取方法，称为FOTO++。第一阶段包括在保留树冠边缘的同时去除多光谱数据中的噪声。之后，借助四元数傅里叶变换的离散形式提取颜色纹理描述符，最后使用支持向量回归方法从纹理指数推导出生物量估计值。我们的纹理特征是使用一个由来自四元数傅里叶变换幅度的径向谱组成的向量进行建模的。我们进行了多项实验，以研究我们的模型对采集参数的敏感性。我们还在合成图像和喀麦隆森林的真实多光谱图像上评估了其性能。结果表明，我们的模型比经典的傅里叶纹理排序模型（FOTO）对采集参数更具鲁棒性。我们的方案在地上生物量估计方面也更准确。我们强调可以使用四元数小波实现类似的方法。这些结果突出了基于四元数的方法在研究多光谱卫星图像方面的潜力。
