Endmember Extraction Using the Pixel Purity Index (PPI) Algorithm The Pixel Purity Index (PPI) algorithm is used to extract endmember signatures from hyperspectral data cubes. The algorithm identifi

PPI算法用于从高光谱数据立方体中提取端元光谱。该算法通过在降维后的数据上投影随机生成的单位向量,来识别出表现出最大投影值的端元像元。 算法原理 对高光谱数据立方体进行降维处理,可选择PCA(主成分分析)、MNF(最大噪声分数)或不降维三种方式。 生成一定数量的随机单位向量(默认10000个)。 将降维后的数据投影到每个随机单位向量上,记录每个像元点获得的最大投影值。 根据最大投影值

In hyperspectral image analysis, estimating the number of endmembers is a crucial step that affects the accuracy and efficiency of subsequent processing. This post introduces an eigenvalue threshold

基于特征值阈值的端元数估计方法 在高光谱图像分析中，端元数估计是一个关键步骤，它关系到后续处理的准确性和效率。本文介绍了一种基于特征值阈值的端元数估计方法，该方法通过噪声白化和特征值分析来确定高光谱数据中的端元数量。 高光谱图像的噪声白化 首先，我们考虑一个高光谱图像 \(\mathbf{X} \in \mathbb{R}^{M \times B}\)，其中 \(M\) 是空间像素数，\(

端元估计在高光谱图像处理中的重要性 高光谱图像处理领域经常会遇到端元矩阵初始化的问题，而端元矩阵的准确估计对于最终处理结果的性能有着重要的影响。在处理凸优化问题时，初始值的选择对最终结果的影响相对较小。然而，面对非凸优化问题，一个接近全局最优解的初始值能够显著提升算法的性能。 凸优化与非凸优化 端元数量的重要性 在高光谱领域的多种模型中，端元数量扮演着关键角色。例如，给定一个高

Setting up OpenCV C++ on Windows Subsystem for Linux (WSL) involves several steps, including installing WSL, installing necessary dependencies, downloading the OpenCV source code, and compiling and

This blog post covers the basics of OpenCV, a popular computer vision library. It explains how to load, display, and save images using OpenCV functions. Additionally, it delves into the Mat class, w

Introduction You can set up a Rust environment on either Windows or Linux. After searching online, I found that setting up a Rust environment on Linux is much quicker than on Windows, but I still u

Setting up a Proxy for WSL2 WSL2 (Windows Subsystem for Linux 2) utilizes a virtual machine approach, separating its network from Windows. This means that network traffic no longer shares the same

Ever found yourself torn between the sleek, user-friendly experience of Windows and the robustness and open-source allure of Linux? Fret not, for you can have the best of both worlds with a seamless