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基于Python的K-Means遥感影像聚类

来源：博客园


(资料图)

import numpy as npfrom sklearn import clusterfrom osgeo import gdal, gdal_arrayimport matplotlib.pyplot as pltgdal.UseExceptions()gdal.AllRegister()
img_ds = gdal.Open("./raster/LC8_2020.tif", gdal.GA_ReadOnly)band = img_ds.GetRasterBand(2)img = band.ReadAsArray()
img_ds.RasterXSize
942
X = img.reshape((-1,1))
k_means = cluster.KMeans(n_clusters=8)k_means.fit(X)X_cluster = k_means.labels_X_cluster = X_cluster.reshape(img.shape)X_cluster.shape
(920, 942)
plt.figure(figsize=(20,20))plt.imshow(X_cluster, cmap="hsv")plt.show()

加入多个波段

img_ds = gdal.Open("./raster/LC8_2020.tif", gdal.GA_ReadOnly)driver = gdal.GetDriverByName("GTiff")img = np.zeros((img_ds.RasterYSize, img_ds.RasterXSize, img_ds.RasterCount),               gdal_array.GDALTypeCodeToNumericTypeCode(img_ds.GetRasterBand(1).DataType))[cols, rows] = img[:, :, 0].shapetrans = img_ds.GetGeoTransform()proj = img_ds.GetProjection()for b in range(img.shape[2]):    img[:, :, b] = img_ds.GetRasterBand(b + 1).ReadAsArray()
new_shape = (img.shape[0] * img.shape[1], img.shape[2])
X = img[:, :, :3].reshape(new_shape)
(104.769919, 0.0002827044278131621, 0.0, 24.076882, 0.0, -0.0002827880434782616)
k_means = cluster.KMeans(n_clusters=8)k_means.fit(X)X_cluster = k_means.labels_X_cluster = X_cluster.reshape(img[:, :, 0].shape)
plt.figure(figsize=(20,20))plt.imshow(X_cluster, cmap="hsv")plt.show()
MB_KMeans = cluster.MiniBatchKMeans(n_clusters=8)MB_KMeans.fit(X)X_cluster = MB_KMeans.labels_X_cluster = X_cluster.reshape(img[:, :, 0].shape)
plt.figure(figsize=(20,20))plt.imshow(X_cluster, cmap="hsv")plt.show()
​​
保存至硬盘
out_img = driver.Create("E:/Desktop/test.tif", rows, cols, 1, gdal.GDT_Byte)out_img.SetGeoTransform(trans)out_img.SetProjection(proj)out_img.GetRasterBand(1).WriteArray(X_cluster)out_img.FlushCache()
                 

关键词：         
       遥感影像