


python gdal tutorial: map algebra and writing of raster data
Take the calculation of NDVI as an example:
NDVI=(NIR-RED)/(NIR+RED)
where NIR is band 3 and RED is band 2
The programming points are as follows:
1. Read band 3 into the array data3, read band 2 into the array data2
2. The calculation formula is:
3. When data3 and data2 are both 0 (for example, 0 represents NODATA), a division by 0 error will occur, causing the program to crash. You need to use mask with choose to remove the 0 value
The code is as follows, there are only 4 lines
data2 = band2.ReadAsArray(0, 0, cols,rows).astype(Numeric.Float16)
data3 = band3.ReadAsArray(0 , 0, cols,rows).astype(Numeric.Float16)
mask = Numeric.greater(data3 + data2, 0)
ndvi = Numeric.choose(mask, (-99, (data3 - data2) / (data3 + data2)))
Create a new raster dataset
Write the data just calculated into the new raster dataset
First, copy a data driver:
driver = inDataset.GetDriver()
Create a new data set afterwards
Create(
The default value of bands is 1, and the default value of GDALDataType is The default type is GDT_Byte, for example
outDataset = driver.Create(filename, cols, rows, 1, GDT_Float32)
During the execution of this statement, the storage space has been allocated to the hard disk
before writing , you also need to introduce the band object first
outBand = outDataset.GetRasterBand(1)
The band object supports direct writing to the matrix, the two parameters are x-direction offset and y-direction offset
outBand.WriteArray(ndvi, 0 , 0)
The following example summarizes the block-by-block writing method this time and last time
xBlockSize = 64
yBlockSize = 64
for i in range(0, rows, yBlockSize):
if i + yBlockSize < rows:
numRows = yBlockSize
else:
numRows = rowsnumRows = rows –– ii
for j in range(0, cols, xBlockSize) :
if j + xBlockSize < cols:
numCols = xBlockSize
outBand.WriteArray(outData, j, i)The band object can set the NoData valueoutBand.SetNoDataValue(-99)You can also read the NoData valueND = outBand.GetNoDataValue()Calculate the statistics of the bandFirst use FlushCache( )Write cached data to disk and then use GetStatistics(
geoTransform = inDataset.GetGeoTransform()outDataset.SetGeoTransform(geoTransform)proj = inDataset.GetProjection()outDataset.SetProjection(proj)Create pyramidsSet Imagine style pyramids
gdal.SetConfigOption ('HFA_USE_RRD', 'YES')Force to build pyramidsoutDataset.BuildOverviews(overviewlist=[2,4, 8,16,32,64,128])Stitching of images1. Read the number of rows and columns, origin (minX, maxY), pixel length, pixel width, and calculate the coordinate range maxX1 = minX1 + (cols1 * pixelWidth) minY1 = maxY1 + (rows1 * pixelHeight) 2 . Calculate the coordinate range of the output image: minX = min(minX1, minX2, …) maxX = max(maxX1, maxX2, …)minY = min(minY1, minY2, …) maxY = max(maxY1, maxY2, …)3. Calculate the number of rows and columns of the output image: cols = int((maxX – minX) / pixelWidth)rows = int((maxY – minY) / abs(pixelHeight)4. Create and initialize the output image5. For each image to be stitched: Calculate the offset valuexOffset1 = int((minX1 - minX) / pixelWidth)yOffset1 = int((maxY1 - maxY) / pixelHeight) Read the data and write it according to the offset calculated above6. For the output image: calculate statistics, set geotransform: [minX, pixelWidth, 0, maxY, 0, pixelHeight], set projection, and create pyramids
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