def ndfi_post_process(im: str, mean_it: str, min_gt: str, output: str) -> None:
"""
对的得到的ndfi经阈值划分后的二值图像进行后处理
通过得到的掩膜图像去除后向散射系数均值小于0.015的和最小值大于0.03的
再进行开运算
:param im:
:param mean_it:
:param min_gt:
:param output
:return:
"""
ndfi, im_width, im_height, im_bands = readImage(im, 1)
mean_it, mean_width, mean_height, mean_bands = readImage(mean_it, 1)
min_gt, min_width, min_height, min_bands = readImage(min_gt, 1)
# 去除后向散射均值小于0.015（可认定为永久水体的部分）
# 去除后向散射最小值大于0.3（在洪涝中发生了什么，但没有达到受灾的水平）
temp = np.zeros((im_height, im_width), np.uint8)
for i in range(0, im_height):
for j in range(0, im_width):
if ndfi[i][j] == 1 and mean_it[i][j] == 1 and min_gt[i][j] == 1:
temp[i][j] = 1
else:
temp[i][j] = 0
array2Raster(temp, output, refImg=im)
def ndfi(pre_images: Union[str, list],
post_images: Union[str, list]) -> Union[np.ndarray, list]:
"""
有灾前灾后图像计算NDFI，要求所有数据在空间上是对齐的
:param pre_images:
:param post_images:
:return:
"""
# 存储灾前灾后影像的像素值
pre_array = []
post_array = []
for img in pre_images:
if isinstance(img, str):
im, im_width, im_height, im_bands = readImage(img, 2)
pre_array.append(im)
for img in post_images:
if isinstance(img, str):
im, im_width, im_height, im_bands = readImage(img, 2)
post_array.append(im)
min_ref_flood = np.amin(pre_array + post_array, 0)
mean_ref = np.mean(pre_array, 0)
# 分析NDFI结果
# 找出后向散射系数均值小于0.015
# 最小值大于0.03的
mean_it = np.ones((im_height, im_width), np.uint8)
min_gt = np.ones((im_height, im_width), np.uint8)
for i in range(0, im_height):
for j in range(0, im_width):
if mean_ref[i][j] <= 15e-3:
mean_it[i][j] = 0
else:
mean_it[i][j] = 1
if min_ref_flood[i][j] >= 3e-2:
min_gt[i][j] = 0
else:
min_gt[i][j] = 1
# 保存后向散射系数均值小于0.015和最小值大于0.03的为二值图像之后用作掩膜
array2Raster(
mean_it,
'/home/Tuotianyu/数据/paper_based/mean_it_0.015_0608_0620_0714_0720.tif',
refImg=
'S1B_IW_GRDH_1SDV_20200608T101815_20200608T101851_021941_029A3A_7867.zip.tif'
)
array2Raster(
min_gt,
'/home/Tuotianyu/数据/paper_based/min_gt_0.015_0608_0620_0714_0720.tif',
refImg=
'S1B_IW_GRDH_1SDV_20200608T101815_20200608T101851_021941_029A3A_7867.zip.tif'
)
return (mean_ref - min_ref_flood) / (mean_ref + min_ref_flood)
def erase_small_area(img: str, threshold: int, output: str) -> None:
"""
消除NDFI提取洪涝区域中像素个数较小的像素族
:param img: 洪涝提取结果
:param threshold: 个数小于阈值的像素族将会被清楚
:param output: 输出路径
:return:
"""
data_set = gdal.Open(img)
im = data_set.GetRasterBand(1).ReadAsArray()
print('height:', len(im), 'width:', len(im[0]))
ret, thresh = cv2.threshold(im, 0, 255, cv2.THRESH_BINARY)
cv2.namedWindow('im', cv2.WINDOW_NORMAL)
cv2.imshow('im', thresh)
cv2.waitKey(0)
# 寻找二值图像中的轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
n = len(contours)
print("'Contours' number:", n)
for i in range(0, n):
if cv2.contourArea(contours[i]) < threshold:
# 初始化，记录消除区域的范围
r_min, r_max, c_min, c_max = sys.maxsize, -1, sys.maxsize, -1
for j in range(0, len(contours[i])):
if contours[i][j][0][1] < r_min:
r_min = contours[i][j][0][1]
if contours[i][j][0][1] > r_max:
r_max = contours[i][j][0][1]
if contours[i][j][0][0] < c_min:
c_min = contours[i][j][0][0]
if contours[i][j][0][0] > c_max:
c_max = contours[i][j][0][0]
for j in range(r_min, r_max + 1):
for k in range(c_min, c_max + 1):
im[j][k] = 0
array2Raster(im, output, refImg=img)
# 绘制轮廓
cv2.drawContours(im, contours, -1, (255, 255, 255), 2)
cv2.namedWindow('contours', cv2.WINDOW_NORMAL)
cv2.imshow('contours', im)
cv2.waitKey(0)
def ndfi_post_process3(im: str, water: str, output: str) -> None:
"""
使用阈值分割提取的水体对NDFI提取的洪涝结果进行掩膜
:param im:
:param water:
:param output
:return:
"""
ndfi, im_width, im_height, im_bands = readImage(im, 1)
water, im_width2, im_height2, im_bands2 = readImage(water, 1)
temp = np.zeros((im_height, im_width), np.uint8)
for i in range(0, im_height):
for j in range(0, im_width):
if ndfi[i][j] == 1 and water[i][j] == 1:
temp[i][j] = 1
array2Raster(temp, output, refImg=im)
def threshold_segmentation(sar_im: str, threshold: float, output: str) -> None:
"""
阈值法提取SAR图像水体
:param sar_im:
:param threshold:
:param output
:return:
"""
if isinstance(sar_im, str):
im, im_width, im_height, im_bands = readImage(sar_im, 2)
temp = np.zeros((im_height, im_width), np.uint8)
for i in range(0, im_height):
for j in range(0, im_width):
if im[i][j] < threshold:
temp[i][j] = 0
else:
temp[i][j] = 1
array2Raster(temp, output, refImg=sar_im)
def ndfi_post_process2(im: str, output: str, it=1) -> None:
"""
使用形态学处理
:param im:
:param it： 迭代的次数
:param output:
:return:
"""
ndfi, im_width, im_height, im_bands = readImage(im, 1)
# 使用3×3的滤波器
kernel = np.ones((3, 3), np.uint8)
# 迭代次数
count = it
# 先膨胀
dilation = cv2.dilate(ndfi, kernel)
# 闭运算
closing_im = cv2.morphologyEx(dilation,
cv2.MORPH_CLOSE,
kernel,
iterations=count)
array2Raster(closing_im, output, refImg=im)
根据阈值将图像转换为二值影像
:param image:
:param threshold:
:return:
"""
if isinstance(image, str):
im, im_width, im_height, im_bands = readImage(image, 1)
temp = np.zeros((im_height, im_width), np.uint8)
for i in range(0, im_height):
for j in range(0, im_width):
if im[i][j] >= threshold:
temp[i][j] = 1
else:
temp[i][j] = 0
return temp
else:
print('please provide the path of image!')
return []
if __name__ == '__main__':
import os
os.chdir(r'F:\毕设\数据\第三次试验')
binary_im = raster2binary('ndfi_0608_0620_0702_0714_0720.tif', 0.7)
array2Raster(binary_im,
'binary_0.7_ndfi_0608_0620_0702_0714_0720.tif',
refImg='ndfi_0608_0620_0702_0714_0720.tif')
if __name__ == '__main__':
import os
os.chdir(
r'/home/Tuotianyu/S1_ARD/paper_based_method/home/Tuotianyu/未预处理S1数据')
pre_image = [
'S1B_IW_GRDH_1SDV_20200608T101815_20200608T101851_021941_029A3A_7867.zip.tif',
'S1B_IW_GRDH_1SDV_20200620T101816_20200620T101851_022116_029F8B_298B.zip.tif'
]
post_image = [
'S1B_IW_GRDH_1SDV_20200714T101817_20200714T101852_022466_02AA36_A270.zip.tif',
'S1B_IW_GRDH_1SDV_20200726T101818_20200726T101853_022641_02AF8A_BF3A.zip.tif'
]
ndfi = ndfi(pre_image, post_image)
array2Raster(
ndfi,
'/home/Tuotianyu/数据/paper_based/ndfi_0608_0620_0714_0726_VV.tif',
refImg=
'S1B_IW_GRDH_1SDV_20200608T101815_20200608T101851_021941_029A3A_7867.zip.tif'
)
# 以0.7位阈值将NDFI转换为二值图像
binary_im = raster2binary(
'/home/Tuotianyu/数据/paper_based/ndfi_0608_0620_0714_0726_VV.tif', 0.7)
array2Raster(
binary_im,
'/home/Tuotianyu/数据/paper_based/binary_ndfi_0608_0620_0714_0726_VV.tif',
refImg='/home/Tuotianyu/数据/paper_based/ndfi_0608_0620_0714_0726_VV.tif'
)
ndfi_post_process2(
'/home/Tuotianyu/数据/paper_based/binary_ndfi_0608_0620_0714_0726_VV.tif',
'/home/Tuotianyu/数据/paper_based/2_post_process_ndfi_0608_0620_0714_0726_VV.tif'
)
im_height = data_set.RasterYSize
im_bands = data_set.RasterCount
print('im_width:', im_width, 'im_height:', im_height, 'bands_num:',
im_bands)
# 绿色波段
g = data_set.GetRasterBand(3).ReadAsArray()
# 近红外波段
nir = data_set.GetRasterBand(8).ReadAsArray()
return (g - nir) / (g + nir)
else:
print('please provide the path of image')
return []
if __name__ == '__main__':
import os
os.chdir(r'/home/Tuotianyu/数据/水体提取/Sentinel-2/第一次试验')
image = 'S2A_MSIL2A_20200715T024551_N0214_R132_T50RMT_20200715T084024_s2resampled.tif'
ndwi = ndwi(image)
array2Raster(
ndwi,
's2_water_0715',
refImg=
'S2A_MSIL2A_20200715T024551_N0214_R132_T50RMT_20200715T084024_s2resampled.tif'
)
# 根据阈值将其转换为二值图像
raster2binary('s2_water_0715', 0.2)
# 使用VV的极化方式
r_im, w1, h1, b1 = readImage(reference_im, 2)
else:
print('Please provide the path of image!')
return []
if isinstance(flooded_img, str):
f_im, w2, h2, b2 = readImage(flooded_img, 2)
else:
print('Please provide the path of image!')
return []
d_img = abs(f_im) - abs(r_im)
return d_img
if __name__ == '__main__':
import os
os.chdir(r'/home/Tuotianyu/NDFI_NDFVI/第八次试验预处理数据/home/Tuotianyu/未预处理S1数据')
difference_im = create_d(
'S1B_IW_GRDH_1SDV_20200620T101816_20200620T101851_022116_029F8B_298B.zip.tif',
'S1B_IW_GRDH_1SDV_20200714T101817_20200714T101852_022466_02AA36_A270.zip.tif'
)
array2Raster(
difference_im,
'0620_0714_difference_image.tif',
refImg=
'S1B_IW_GRDH_1SDV_20200714T101817_20200714T101852_022466_02AA36_A270.zip.tif'
)
def opening(im: Union[list, np.ndarray], it: int) -> Union[list, np.ndarray]:
"""
开运算，先腐蚀后膨胀
:param im:读入的影像
:return:
"""
binary_im, im_width, im_height, im_bands = readImage(im, 1)
# 使用3×3的滤波器
kernel = np.ones((3, 3), np.uint8)
# 迭代次数
count = it
# 开运算
opening_im = cv2.morphologyEx(binary_im,
cv2.MORPH_OPEN,
kernel,
iterations=count)
return opening_im
if __name__ == '__main__':
import os
os.chdir(r'F:\毕设\数据\Sentinel-2\第一次试验')
closing_im = closing('binary_0.2_s2_water_0715.tif')
array2Raster(closing_im,
'closing_binary_0.2_s2_water_0715.tif',
refImg='binary_0.2_s2_water_0715.tif')