S-G滤波之包络滤波

由于遥感观测总是受到云层，大气影响，总会造成植被指数骤降问题，包络滤波的就是根据植被的特性，将骤降值认为是错误值，选择最大值进行滤波
SG滤波代码参考&后来&博客

定义滤波函数

import pandas as pd
import matplotlib.pyplot as plt
#%% SG_filter
import numpy as np
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
def sgoal_filter(data,window_size,order):

    if window_size == None:
        window_size = int(len(data)) // 10       
    if window_size % 2 == 0 or window_size == 0: 
        window_size += 1
    
    arr = []
    step = int((window_size-1)/2)
    for i in range(window_size):
        a = []
        for j in range(order):
            y_val = np.power(-step + i, j)
            a.append(y_val)
        arr.append(a)
    
    arr = np.mat(arr)
    arr = arr * (arr.T * arr).I * arr.T
    
    a = np.array(arr[step])
    a = a.reshape(window_size)
    
    data = np.insert(data, 0, [data[0] for i in range(step)])
    data = np.append(data, [data[-1] for i in range(step)])
    
    qlist = []
    for i in range(step, data.shape[0] - step):
        arra = []
        for j in range(-step, step+1):
            arra.append(data[i +j])   
        b = np.sum(np.array(arra) * a)
        qlist.append(b)
    return qlist

ndvi[‘MCD15A3H_006_ndvi_500m’]滤波前图片

initial = np.array(ndvi['MCD15A3H_006_ndvi_500m'])
data_sg = sgoal_filter(ndvi['MCD15A3H_006_ndvi_500m'])
sg_1 = np.array(data_sg)
dev = initial-sg_1
stad = np.sqrt(np.mean(dev**2))
while stad > 0.08:   
    for i in range(len(sg_1)):
        if dev[i] >0:
            sg_1[i] = initial[i]
    initial = sg_1
    sg_1 = np.array(sgoal_filter(sg_1,3,1))
    dev = initial-sg_1
    stad = np.sqrt(np.mean(dev**2))
fig, axes = plt.subplots()
axes.plot(ndvi.Date, sg_1)
axes.plot(ndvi.Date, np.array(ndvi['MCD15A3H_006_Lai_500m']))

结果