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<!DOCTYPE html><html lang="en" data-theme="light"><head><meta charset="UTF-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0,viewport-fit=cover"><title>Hexo</title><meta name="author" content="Shamir Chen"><meta name="copyright" content="Shamir Chen"><meta name="format-detection" content="telephone=no"><meta name="theme-color" content="#ffffff"><meta property="og:type" content="website">
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})(window)</script><meta name="generator" content="Hexo 6.2.0"></head><body><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="avatar-img is-center"><img src="https://i.loli.net/2021/02/24/5O1day2nriDzjSu.png" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="sidebar-site-data site-data is-center"><a href="/archives/"><div class="headline">Articles</div><div class="length-num">48</div></a><a href="/tags/"><div class="headline">Tags</div><div class="length-num">4</div></a><a href="/categories/"><div class="headline">Categories</div><div class="length-num">10</div></a></div><hr class="custom-hr"/></div></div><div class="page" id="body-wrap"><header class="full_page" id="page-header"><nav id="nav"><span id="blog-info"><a href="/" title="Hexo"><span class="site-name">Hexo</span></a></span><div id="menus"><div id="toggle-menu"><a class="site-page" href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="site-info"><h1 id="site-title">Hexo</h1></div><div id="scroll-down"><i class="fas fa-angle-down scroll-down-effects"></i></div></header><main class="layout" id="content-inner"><div class="recent-posts" id="recent-posts"><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/10/25/%E6%96%87%E7%8C%AE%E8%AE%A1%E9%87%8F%E5%88%86%E6%9E%90/" title="文献计量分析">文献计量分析</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-10-25T02:52:33.000Z" title="Created 2023-10-25 10:52:33">2023-10-25</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/others/">others</a></span></div><div class="content">常用的文献分析软件有 Vosviwer, citespace, biblometrix等
Vosviwer在关键词贡献网络的可视化上更方便
时空区图谱（主题路径图）可以反应关键词出现的年份，以及出现年份到目前为止的出现频次
关键词聚类时间线
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/09/22/%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97%E9%A2%84%E6%B5%8B%E5%AD%A6%E4%B9%A0%E6%80%BB%E7%BB%93/" title="时间序列预测学习总结">时间序列预测学习总结</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-09-22T07:06:36.000Z" title="Created 2023-09-22 15:06:36">2023-09-22</time></span></div><div class="content"></div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/09/02/%E8%87%AA%E5%9B%9E%E5%BD%92%E5%B7%AE%E5%88%86%E7%A7%BB%E5%8A%A8%E5%B9%B3%E5%9D%87/" title="自回归差分移动平均">自回归差分移动平均</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-09-02T02:40:39.000Z" title="Created 2023-09-02 10:40:39">2023-09-02</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/others/">others</a></span></div><div class="content">Auto-Regressive Integrated Moving Average自回归移动平均（ARIMA）是时间序列预测中的常用方法，在遥感长时间序列数据（Long Time Series, LTS）处理中有很广泛的应用。
ARIMA是 ARMA（自回归移动平均的）加了差分后的衍生方法。
ARIMA假设时序通过数据自身的相关型描述而不是趋势(trends)和季节性 (seasonality)
ARIMA 由三个部分组成：
AR (Auto regression)：自回归模型，自回归模型描述当前值与历史值之间的关系，用变量自身的历史时间数据对自身进行预测。
I (Integrated)：差分，使用前一时刻的观测值减去当前观测值，目的是使得序列更稳定。如果没有差分这一过程，则AR+MA为自回归移动平均 ARMA（auto-regressive moving average）
MA (Moving average)：移动平均模型，利用观测值与残差（ residual error ）的依赖性
上述三个部分可以表示为3个参数。标准的符号表示是 ARIMA(p,d,q)，p,d,q 参数 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/08/29/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E5%8E%BB%E8%B6%8B%E5%8A%BF/" title="时序数据去趋势">时序数据去趋势</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-29T09:25:13.000Z" title="Created 2023-08-29 17:25:13">2023-08-29</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/others/">others</a></span></div><div class="content">Detrend Time Series Data趋势是时间序列数据中的一个长期增长或降低现象。
在遥感长时间序列数据的研究中，去除这种趋势对于提升模型性能，同时趋势信息也可以作为额外的特征来提高模型性能。
时间序列中的趋势大体可以分为：
绝对趋势（deterministic trends)：连续增长或降低的趋势
随机趋势（stochastic trends）：不连续的增长或降低
如果从趋势出现的时间范围出发，趋势可以分为：
全局趋势（global trend）：存在于整个时间序列上
局部趋势（local trend）：只在局部时间上出现
趋势识别在实际应用中，识别时间序列中的趋势的方法是比较主观的，可以通过对时间序列进行线性回归或者非线性回归，观测回归函数是否有明显的趋势。
移除趋势（Detrend）差分方法差分方法（differencing）是时间序列去趋势的最简单的方法。将每个时间点的数据用该点的值与上一个时间点的值的插值代替：
value(t) = observation(t) - observation(t-1)这种方法在时间序列数据具有线性趋势时效果才会比较好。这个 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/08/27/%E9%81%97%E4%BC%A0%E7%AE%97%E6%B3%95/" title="遗传算法">遗传算法</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-27T12:40:42.000Z" title="Created 2023-08-27 20:40:42">2023-08-27</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/others/">others</a></span></div><div class="content">Genetic Algorithm相关概念染色体和基因(chromosomes and genetics)染色体即为函数的所有可行解，一个可行解中的元素称为基因
如对于函数 $3x+4y+5z<100$ 的可行解为 [1,2,3]、[1,3,2]、[3,2,1]，这三个可行解在遗传算法中称为染色体，每个可行解中的元素称为组成染色体的基因。
适应度函数适应度函数在每次迭代中为生成的染色体打分，来评判染色体的适应度。
交叉(crossover)对两条染色体进行交叉，生成新的染色体。根据适应度选择要进行交叉的染色体。
$染色体i被选择的概率 = 染色体i的适应度 / 所有染色体的适应度之和$
变异(mutation)通过交叉生成新的染色体后，在染色体上随机选择若干个基因，然后随机修改基因的值，从而给现有的染色体引入了新的基因，有利于算法寻找到全局最优解
复制(copy)每次进化中，为了保留上一代优良的染色体，需要将上一代中适应度最高的几条染色体直接原封不动地复制给下一代。假设每次进化都需生成N条染色体，那么每次进化中，通过交叉方式需要生成N-M条染色体，剩余的M条染色体通过复制上一代适 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/08/17/%E5%90%91%E9%87%8F%E7%A9%BA%E9%97%B4%E5%92%8C%E5%AD%90%E7%A9%BA%E9%97%B4-Vectorspace-and-Subspace/" title="Vector spaces and Subspaces">Vector spaces and Subspaces</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-17T01:51:09.000Z" title="Created 2023-08-17 09:51:09">2023-08-17</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/linear-algebra/">linear algebra</a></span></div><div class="content">向量空间（Spaces of Vectors)和子空间（Subspaces)首先需要了解一些概念和定义
1.标准的 n-dimension 空间 $R^n$ 包含所有实数向量，并且每个向量有 n 个 元素；如 ： 二维平面空间：$R^2$ 的一个子空间：
\left[
\begin{matrix}
1 & 2 & 3 \\
4 & 5 & 6 \\
\end{matrix}
\right]2.如果 v 和 w在向量空间 S 中， 那么 v和 w 的线性组合 $cv+dw$必定在 S 中
3.空间 S 中的 “向量”可以是 矩阵 或 x 的函数。
4.$R^n$ 的一个子空间是 $R^n$内的一个向量空间，如：$R^2$中的 $y=3x$
5.A（矩阵） 的列空间是 A 中列的所有线性组合，即：$R^m$的一个列空间
6.当 b 在列向量空间 $C(A)$中时，$Ax = b$ 有解，因为列空间（column space）包含了$Ax$的所有向量（Ax本质是对A的列向量进行线性组合，Ax就是A的列空间）。即当 b 是 A 的各列的线性组合，才存在对应的 x ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/08/15/Graph-Neural-Network/" title="Graph Neural Network">Graph Neural Network</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-08-15T08:52:26.000Z" title="Created 2023-08-15 16:52:26">2023-08-15</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/Deep-Learning/">Deep Learning</a></span></div><div class="content"> </div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/07/28/GEE-Timeseries-Interpolation/" title="GEE_Timeseries_Interpolation">GEE_Timeseries_Interpolation</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-07-28T09:25:17.000Z" title="Created 2023-07-28 17:25:17">2023-07-28</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/GEE/">GEE</a></span></div><div class="content">光学遥感影像去云后后会留下空缺，本文记录如何在GEE中对空缺值进行填充
去云根据网上的资料，GEE中常用的去云方法有三种：
利用光学遥感影像的质量评估（Quality assesment, QA）波段
如 S2（sentinel-2）的”QA60”波段，包含了云、云影、卷云、水体等信息。
参考：【遥感】遥感影像中的QA波段（质量评估波段）存储方式介绍qa_pixel和bqa碧空慧眼的博客-CSDN博客
GEE的S2去云：
12345678function rmCloudByScore(image, thread) { var preBands = ["B2","B3","B4","B8","B11","B12"]; var newBands = ['blue','green','red','nir','swir1','swir2' ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/07/18/%E9%A9%AC%E5%B0%94%E7%A7%91%E5%A4%AB%E9%93%BE-Markov-chain/" title="马尔科夫链(Markov_chain)">马尔科夫链(Markov_chain)</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-07-18T06:39:05.000Z" title="Created 2023-07-18 14:39:05">2023-07-18</time></span><span class="article-meta"><span class="article-meta-separator">|</span><i class="fas fa-inbox"></i><a class="article-meta__categories" href="/categories/others/">others</a></span></div><div class="content">Markov chain马尔可夫链在很多时间序列模型中得到广泛的应用，比如循环神经网络 RNN，隐式马尔可夫模型 HMM 等
马尔可夫链是一个数学系统，根据特定的概率规则从状态空间中的一个状态到另一个状态转换的随机过程。
马尔科夫链中，无论当前到下一状态的转变是如何进行的，下一个状态的概率是固定的。也就是说，即下一状态的概率分布只能由当前状态决定，在时间序列中它前面的事件均与之无关。
状态空间或者所有可能状态 可以是任何事情：数字，字母，天气状况，股票表现。
数学定义对于任意的正整数 n, 和可能的状态 $i_0, i_1, …, i_n$ （随机变量）
P(X_n = i_n|X_{n-1}=i_{n-1}) = P(X_n=i_n|X_0=i_0,X_1=i_1,...,X_{n-1}=i_{n-1})也就是说，只需要知道上一时刻的状态，就可以知道当前时刻的概率分布。
转移概率矩阵（transition matrices）对于在时刻 $t$ 的 ${X}$，转移概率矩阵$P_t$ 包含了不同状态之间的转换信息。
给定一个状态空间 S 按顺序组成的矩阵行 和 列，矩阵$P_t$中第 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2023/07/07/PCA/" title="PCA">PCA</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2023-07-07T12:00:10.000Z" title="Created 2023-07-07 20:00:10">2023-07-07</time></span></div><div class="content">主成分分析（Principle component analysis, PCA)主成分分析是一种数据降维方法。数据中的多个变量之间可能存在相关性，增加了问题分析的复杂性
由于各变量之间存在一定的相关关系，因此可以考虑将关系紧密的变量变成尽可能少的新变量，使这些新变量是两两不相
关的，那么就可以用较少的综合指标分别代表存在于各个变量中的各类信息。
PCA算法有两种实现方法：基于特征值分解协方差矩阵实现PCA算法、基于SVD分解协方差矩阵实现PCA算法。
基于特征值分解协方差矩阵实现PCA算法对于需要降维的数据 X = {x_1,x_2,x_3,...,x_n}，将X降到K维
1) 去平均值（即去中心化），即每一位特征减去各自的平均值2) 计算协方差矩阵\frac 1 n X X^T3) 用特征值分解法求协方差矩阵 \frac 1 n X X^T 的特征值和特征向量4) 对特征值从大到小排序，选择其中最大的k个。然后将其对应的k个特征向量分别作为行向量组成特征向量矩阵P。5) 将数据转换到k个特征向量构建的新空间中，即Y=PX。
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