python - 我如何使用NumpyArrayToRaster()从4波段图像中提取3波段？

我需要从4波段图像中提取3个波段。我正在使用一个名为NumpyArrayToRaster（）的函数，它只接受3个波段的图像。我如何使它为4波段图像工作？
这是我现在的密码-

import arcpy
arcpy.CheckOutExtension("Spatial")
from PIL import Image

import matplotlib.pyplot as plt
import numpy as np
from skimage import io
from skimage.segmentation import quickshift

arcpy.env.overwriteOutput = True

# The input 4-band NAIP image
img = r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\clip4.tif'


# Convert image to numpy array
imgarr = io.imread(img)
print imgarr
print imgarr.shape
print imgarr.dtype

# Run the quick shift segmentation
segments = quickshift(imgarr, kernel_size=3, convert2lab=False, max_dist=6, ratio=0.5)
print("Quickshift number of segments: %d" % len(np.unique(segments)))


# View the segments via Python
plt.imshow(segments)
print segments
print segments.shape
print type(segments)
print segments.dtype


# Get raster metrics for coordinate info
imgRaster = arcpy.sa.Raster(img)

# Lower left coordinate of block (in map units)
mx = imgRaster.extent.XMin
my = imgRaster.extent.YMin
sr = imgRaster.spatialReference

'''
# Note the use of arcpy to convert numpy array to raster
seg = arcpy.NumPyArrayToRaster(segments, arcpy.Point(mx,my), imgRaster.meanCellWidth, imgRaster.meanCellHeight)

outRaster = r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\segments_clip4.tif'
seg_temp = seg.save(outRaster)
arcpy.DefineProjection_management(outRaster, sr)
'''

# Calculate NDVI from bands 4 and 3
b4 = arcpy.sa.Raster(r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\clip4.tif\Band_4')
b3 = arcpy.sa.Raster(r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\clip4.tif\Band_3')
ndvi = arcpy.sa.Float(b4-b3) / arcpy.sa.Float(b4+b3)
print ndvi

# Extract NDVI values based on image object boundaries
zones = arcpy.sa.ZonalStatistics(segments, "VALUE", ndvi, "MEAN")
zones.save(r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\zones_clip4.tif')

# Classify the segments based on NDVI values
binary = arcpy.sa.Con(zones < 20, 1, 0)
binary.save(r'C:\Users\Alekhya\Desktop\Krishna\NRSC\processed image\newclip4\classified_clip4.tif')

最佳答案

在阅读代码时，我意识到需要计算NDVI值。也许您可以稍微修改一下您的方法，而不是使用函数NumpyArrayToRaster(），您可以使用更简单的方法？
在这里，我提供我的代码：
-从多个日期（多波段合成）读取单个哨兵数据堆栈
-使用rasterName/Band_X确定NDVI计算所需的波段
-从波段计算NDVI
-将每个日期的NDVIs保存到输出文件夹，保留其名称和日期
在陆地卫星上，NDVI的波段是波段4和波段3
我的代码：

# calculate NDVI from sentinel data
# list raster and calculate NDVI per each raster individually

# import modules
import arcpy, string

# import environmental settings
from arcpy import env
from arcpy.sa import *

# check out spatial extension
arcpy.CheckOutExtension("spatial")
arcpy.env.overwriteOutput = True

# add workspace
env.workspace = "C:/Users/input"

# List rasters
rasters = arcpy.ListRasters("*", "TIF")

# Output directory
outWd = "C:/Users/output/ndvi"

# calculate ndvi for every sentinel raster
for raster in rasters:
    # define ndvi outputs
    outNDVI = outWd + "/"+ raster.replace(".tif", "_ndvi.tif")
    print "outNDVI is " + outNDVI

    # specify inputs for ndvi and final output
    # NDVI takes NIR and Red, which are in Sentinel  Band 4 and Band 8
    Red = raster + '\Band_4'
    NIR = raster + '\Band_8'

    # Create Numerator and Denominator rasters as variables, and
    # NDVI as output
    Num = arcpy.sa.Float(Raster(NIR) - Raster(Red))
    Denom = arcpy.sa.Float(Raster(NIR) + Raster(Red))
    NDVI = arcpy.sa.Divide(Num, Denom)
    print "NDVI calculating"

    # save results output
    NDVI.save(outNDVI)
    print "NDVI saved"