绘制出来的卫星云图
全圆盘真彩图:
全圆盘单通道红外图:
数据准备
数据格式说明:http://fy4.nsmc.org.cn/data/cn/data/realtime.html数据下载地址:http://satellite.nsmc.org.cn/portalsite/Data/DataView.aspx?SatelliteType=1&SatelliteCode=FY4A#
本人使用的是4000M的全圆盘数据,下载数据需要申请账号
解析HDF数据
from netCDF4 import Dataset
import h5py
# 两种解析方式 netCDF4 和 h5py 直接用conda安装
hdf_data_path = "/Users/Downloads/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20200805080000_20200805081459_4000M_V0001.HDF"
# h5py 解析
hdf_obj = h5py.File(hdf_data_path, "r")
#打印文件里所有属性 属性含义自行查看数据说明格式
print(hdf_obj.keys())
# 通道1数据
print(hdf_obj['NOMChannel01'][:])
# netCDF4 解析
nc_obj = Dataset(hdf_data_path)
#打印文件里所有属性
print(nc_obj.variables.keys())
# 通道1数据
print(nc_obj.variables['NOMChannel01'][:])
绘制单通道云图
from netCDF4 import Dataset
import matplotlib.pyplot as plt
hdf_data_path = "/Users/Downloads/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20200805080000_20200805081459_4000M_V0001.HDF"
nc_obj = Dataset(hdf_data_path)
type = nc_obj.variables.keys()
print(type)
keyword = "NOMChannel"
for k in type:
if str(k).find(keyword) == 0:
data = nc_obj.variables[k][:]
plt.imshow(data, cmap='gray')
plt.title(k)
plt.axis('off')
plt.show()
print("通道" + k + "绘制完成")
运行绘制出14个通道图:
绘制真彩云图
官方卫星真彩云图:http://fy4.nsmc.org.cn/portal/cn/theme/FY4A.html
上面是官方绘制出来的,绘制这样一张云图需要多通道数据融合,搜索到一篇论文,里面详细介绍了云图的合成!
FY-4A多通道扫描辐射成像仪评价与图像合成 论文地址:http://www.doc88.com/p-8866426031707.html
python数字图像处理:图像数据类型及颜色空间转换:
绘制真彩图流程:
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from skimage import io, data, img_as_float, img_as_ubyte, img_as_uint, img_as_int
import cv2
hdf_data_path = "/Users/Downloads/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20200805080000_20200805081459_4000M_V0001.HDF"
nc_obj = Dataset(hdf_data_path)
type = nc_obj.variables.keys()
print(type)
B = nc_obj.variables['NOMChannel01'][:]
G = nc_obj.variables['NOMChannel02'][:]
R = nc_obj.variables['NOMChannel03'][:]
# 将数据类型转成int16
B = img_as_int(B)
G = img_as_int(G)
R = img_as_int(R)
# opencv 将rgb三个通道融合
img3 = cv2.merge([R, G, B])
img3 = exposure.adjust_log(img3, inv=True)#调整对比度
img3 = exposure.adjust_gamma(img3, 1.2) # 图像调暗
T = 2
for i in range(len(img3)):
for j in range(len(img3[0])):
r = img3[i][j][0]
g = img3[i][j][1]
b = img3[i][j][2]
img3[i][j] = (r * 0.6, g, b)
if r / g > T:
img3[i][j] = (g, r * 0.7, b)
plt.imshow(img3, )
plt.axis('off')
plt.show()
#
我这里只绘制出来的效果还是差点,最近比较忙有时间再解决吧!
上述问题,如果有解决了的同学,麻烦通知我一声
绘制中国地区的卫星云图
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import math
from numpy import deg2rad, rad2deg, arctan, arcsin, tan, sqrt, cos, sin
import numpy as np
from mpl_toolkits.basemap import Basemap
hdf_data_path = "/Users/Downloads/FY4A-_AGRI--_N_DISK_1047E_L1-_FDI-_MULT_NOM_20200805080000_20200805081459_4000M_V0001.HDF"
ch_map = "/Users/map/中国地图/国界/bou1_4l"
ea = 6378.137 # 地球的半长轴[km]
eb = 6356.7523 # 地球的短半轴[km]
h = 42164 # 地心到卫星质心的距离[km]
λD = deg2rad(104.7) # 卫星星下点所在经度
# 列偏移
COFF = {"0500M": 10991.5,
"1000M": 5495.5,
"2000M": 2747.5,
"4000M": 1373.5}
# 列比例因子
CFAC = {"0500M": 81865099,
"1000M": 40932549,
"2000M": 20466274,
"4000M": 10233137}
LOFF = COFF # 行偏移
LFAC = CFAC # 行比例因子
def latlon2linecolumn(lat, lon, resolution):
"""
经纬度转行列
(lat, lon) → (line, column)
resolution:文件名中的分辨率{'0500M', '1000M', '2000M', '4000M'}
line, column
"""
# Step1.检查地理经纬度
# Step2.将地理经纬度的角度表示转化为弧度表示
lat = deg2rad(lat)
lon = deg2rad(lon)
# Step3.将地理经纬度转化成地心经纬度
eb2_ea2 = eb ** 2 / ea ** 2
λe = lon
φe = arctan(eb2_ea2 * tan(lat))
# Step4.求Re
cosφe = cos(φe)
re = eb / sqrt(1 - (1 - eb2_ea2) * cosφe ** 2)
# Step5.求r1,r2,r3
λe_λD = λe - λD
r1 = h - re * cosφe * cos(λe_λD)
r2 = -re * cosφe * sin(λe_λD)
r3 = re * sin(φe)
# Step6.求rn,x,y
rn = sqrt(r1 ** 2 + r2 ** 2 + r3 ** 2)
x = rad2deg(arctan(-r2 / r1))
y = rad2deg(arcsin(-r3 / rn))
# Step7.求c,l
column = COFF[resolution] + x * 2 ** -16 * CFAC[resolution]
line = LOFF[resolution] + y * 2 ** -16 * LFAC[resolution]
return np.rint(line).astype(np.uint16), np.rint(column).astype(np.uint16)
# 中国范围
x_min = 11
x_max = 54.75
y_min = 73.31
y_max = 135.91
column = math.ceil((x_max - x_min) / 0.04)
row = math.ceil((y_max - y_min) / 0.04)
print(row, column)
ynew = np.linspace(y_min, y_max, row) # 获取网格y
xnew = np.linspace(x_min, x_max, column) # 获取网格x
xnew, ynew = np.meshgrid(xnew, ynew) # 生成xy二维数组
data_grid = np.zeros((row, column)) # 声明一个二维数组
keyword = "NOMChannel"
nc_obj = Dataset(hdf_data_path)
type = nc_obj.variables.keys()
print(type)
print("--------------------------------------------")
index = {}
r_data = {}
for k in type:
if str(k).find(keyword) == 0:
value = nc_obj.variables[k][:]
for i in range(row):
for j in range(column):
lat = xnew[i][j]
lon = ynew[i][j]
fy_line = 0
fy_column = 0
if index.get((lat, lon)) == None:
# 查找行列并记录下来下次循环使用
fy_line, fy_column = latlon2linecolumn(lat, lon, "4000M")
index[(lat, lon)] = fy_line, fy_column
else:
fy_line, fy_column = index.get((lat, lon))
data_grid[i][j] = value[fy_line, fy_column]
r_data[k] = data_grid
img = plt.figure()
ax = img.add_subplot(111)
m = Basemap(llcrnrlon=y_min, llcrnrlat=x_min, urcrnrlon=y_max, urcrnrlat=x_max)
m.readshapefile(ch_map, 'states', drawbounds=True)
p = plt.contourf(ynew, xnew, data_grid, cmap="gray", )
plt.axis('off')
plt.show()
print("通道" + k + "绘制完成")
运行效果:
ok !!! 完事这样绘制出来的图片就可以叠加到地图上了。
代码看着简单,其实源文件里没让大家看,很多坑我这就不说了,希望大家直接避过坑,折腾我两个星期,不容易啊。
绘制出来的图片与官方对比