森林火点识别算法代码

.vscode/launch.json

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+{
+    "configurations": [
+        
+        {
+            "name": "Python: File",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "justMyCode": true
+        }
+    ]
+}

threshold.py

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+from osgeo import gdal
+import cv2
+import numpy as np
+from osgeo import gdal
+
+def main():
+    # 打开影像文件
+    image_00_00_path = "E:/vscode_code/fire_code/HS_H09_20230801_0000/HS_H09_20230801_0000.tif"  # 替换为你的影像路径
+    dataset_00 = gdal.Open(image_00_00_path)
+
+    if dataset_00 is None: 
+        print("HS_H09_20230801_0000.tif.")
+        return
+    
+    image_00_10_path = "E:/vscode_code/fire_code/HS_H09_20230801_0010/HS_H09_20230801_0010.tif"  # 替换为你的影像路径
+    dataset_10 = gdal.Open(image_00_10_path)
+
+    if dataset_10 is None: 
+        print("HS_H09_20230801_0010.tif.")
+        return
+    
+    # 获取00时00分 band 7 和 band 14 的 temperature 数据
+    band7_00_00_temperature = dataset_00.GetRasterBand(7).ReadAsArray()
+    band14_00_00_temperature = dataset_00.GetRasterBand(14).ReadAsArray()
+
+    # 获取00时10分 band 7 和 band 14 的 temperature 数据
+    band7_00_10_temperature = dataset_10.GetRasterBand(7).ReadAsArray()
+    band14_00_10_temperature = dataset_10.GetRasterBand(14).ReadAsArray()
+    
+    #计算band7，band14在00分到10分的差值
+    band7_temperature_difference = band7_00_10_temperature - band7_00_00_temperature
+    band14_temperature_difference = band14_00_10_temperature - band14_00_00_temperature
+
+    #计算band7与band14在00分和10分的差值
+    band7_band14_00temperature_difference = band7_00_00_temperature - band14_00_00_temperature
+    band7_band14_10temperature_difference = band7_00_10_temperature - band14_00_10_temperature
+
+    #计算10分的band7与00分的band14的差值
+    band7_band14_0010temperature_difference = band7_00_10_temperature - band14_00_00_temperature
+
+    #判断火点依据
+    fire_point_image = []
+
+    for y in range(len(band7_00_00_temperature)):
+        row = []
+        for x in range(len(band7_00_00_temperature[y])):
+            if band7_00_00_temperature[y][x] > 260 and band7_00_10_temperature[y][x] > 260 and band7_temperature_difference[y][x]>15 and band7_band14_10temperature_difference[y][x] >10 and band14_temperature_difference[y][x] >-1 and band7_band14_0010temperature_difference[y][x] >12:
+                row.append(0)  # 白色
+            else:
+                row.append(225)  # 黑色
+        fire_point_image.append(row)
+
+    fire_point_image = np.array(fire_point_image, dtype=np.uint8)
+
+    # 保存火点图像到本地
+    cv2.imwrite("fire_point_image_2.tif", fire_point_image)
+
+     # Open target TIFF for update
+    target_dataset = gdal.Open("fire_point_image_2.tif", gdal.GA_Update) 
+    dataset_00_geo_transform = dataset_00.GetGeoTransform()
+
+     # Apply the source geo-transform to the target TIFF
+    target_dataset.SetGeoTransform(dataset_00_geo_transform)
+
+    # Close the datasets to save changes
+    dataset_00 = None
+    dataset_10 = None
+    target_dataset = None
+    
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main()

threshold_adapt.py

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+from osgeo import gdal
+import cv2
+import numpy as np
+from osgeo import gdal
+import ephem
+
+# 水体掩膜
+def is_water(reflectance_band4, reflectance_band5, threshold_reflectance_4=0.05, threshold_reflectance_5=0.15):
+    if reflectance_band4 < threshold_reflectance_4 and reflectance_band5 < threshold_reflectance_5:
+        return True
+    else:
+        return False
+#云掩膜
+def is_cloud(reflectance_band3, reflectance_band5, band14_00_00_temperature, threshold_reflectance_35=0.9, threshold_temperature_14=265):
+    if reflectance_band3 + reflectance_band5 > threshold_reflectance_35 and band14_00_00_temperature < threshold_temperature_14:
+        return True
+    else:
+        return False
+# 太阳光耀
+def calculate_solar_azimuth(latitude, longitude, observation_time):
+    observer = ephem.Observer()
+    observer.lat = str(latitude)
+    observer.lon = str(longitude)
+    observer.date = observation_time  # Set the observation time
+
+    sun = ephem.Sun()
+    sun.compute(observer)
+
+    azimuth = float(sun.az) * 180 / ephem.pi
+    if azimuth < 0:
+        azimuth += 360
+
+    return azimuth
+
+def main():
+    # 打开影像文件
+    image_00_00_path = "E:/Vscode_code/HS_H09_20230801_0000/HS_H09_20230801_0000.tif"  # 替换为你的影像路径
+    dataset_00 = gdal.Open(image_00_00_path)
+
+    if dataset_00 is None: 
+        print("HS_H09_20230801_0000.tif.")
+        return
+    
+    # 水体掩膜
+    #获取波段4和波段5的表观反射率
+    band4_00_00_radiance = dataset_00.GetRasterBand(4).ReadAsArray()
+    band5_00_00_radiance = dataset_00.GetRasterBand(5).ReadAsArray()
+
+    # Specify your thresholds
+    threshold_reflectance_4 = 0.05
+    threshold_reflectance_5 = 0.15
+
+    #生成云掩膜
+    #获取波段3和波段5的表观反射率，波段14的亮温
+    band3_00_00_radiance = dataset_00.GetRasterBand(3).ReadAsArray()
+    band5_00_00_radiance = dataset_00.GetRasterBand(5).ReadAsArray()
+    band14_00_00_temperature = dataset_00.GetRasterBand(14).ReadAsArray()
+
+    # Specify your thresholds
+    threshold_reflectance_35 = 0.9
+    threshold_temperature_14 = 265
+    
+     #计算太阳方位角
+    # Get the geotransformation information
+    geo_transform = dataset_00.GetGeoTransform()
+
+    # Get image size (number of rows and columns)
+    rows = dataset_00.RasterYSize
+    cols = dataset_00.RasterXSize
+
+    # Iterate through each pixel and convert row/col to geographic coordinates
+    for row in range(rows):
+        for col in range(cols):
+            x_geo = geo_transform[0] + col * geo_transform[1] + row * geo_transform[2]
+            y_geo = geo_transform[3] + col * geo_transform[4] + row * geo_transform[5]
+
+    # Replace with the actual latitude, longitude, and observation time
+    latitude = y_geo
+    longitude = x_geo
+    observation_time = "2023/08/01/00/00/00"  # Replace with the actual observation time
+
+    solar_azimuth = calculate_solar_azimuth(latitude, longitude, observation_time)
+
+    # Create an empty mask to store water,cloud,sun pixels
+    water_cloud_sun_mask_image = []
+
+    # Iterate through each pixel and determine if it's water,cloud,sun or not
+    for y in range(len(band3_00_00_radiance)):
+        row_mask = []
+        for x in range(len(band3_00_00_radiance[y])):
+
+            if is_water(band4_00_00_radiance[y, x], band5_00_00_radiance[y, x], threshold_reflectance_4, threshold_reflectance_5):
+                row_mask.append(0)
+
+            elif is_cloud(band3_00_00_radiance[y, x], band5_00_00_radiance[y, x], band14_00_00_temperature[y, x], threshold_reflectance_35, threshold_temperature_14):
+                row_mask.append(0)
+
+            elif 165 < solar_azimuth < 200:
+                row_mask.append(0)
+
+            else:
+                row_mask.append(255)
+
+        water_cloud_sun_mask_image.append(row_mask)
+    
+    # 遥感数据，假设你已经有了一个4通道的温度数据和对应的14通道数据
+    band7_00_00_temperature = dataset_00.GetRasterBand(7).ReadAsArray()
+    band14_00_00_temperature = dataset_00.GetRasterBand(14).ReadAsArray()
+
+    # 定义阈值
+    day_threshold = 360  # 白天温度阈值
+    night_threshold = 320  # 夜晚温度阈值
+    background_expansion = 7  # 背景区域扩展步长
+
+    # 计算晴空植被像元数量
+    # 计算NDVI
+    band4_00_00_radiance = dataset_00.GetRasterBand(4).ReadAsArray()
+    band3_00_00_radiance = dataset_00.GetRasterBand(3).ReadAsArray()              
+
+    # 禁用特定的警告
+    np.seterr(divide='ignore', invalid='ignore')
+
+    # 计算NDVI，将无效值设为0
+    ndvi = np.where((band4_00_00_radiance + band3_00_00_radiance) != 0, (band4_00_00_radiance - band3_00_00_radiance) / (band4_00_00_radiance + band3_00_00_radiance), 0)
+
+    # 遥感数据，假设你已经有了一个植被指数的数据，比如Normalized Difference Vegetation Index (NDVI)
+    ndvi_data = ndvi  # 替换为你的植被指数数据
+
+    # 定义阈值，这个阈值通常需要根据具体的数据和应用场景来设置
+    vegetation_threshold = 0.2  # 例如，设置为0.2表示NDVI值大于0.2的像元被认为是植被
+
+    # 定义植被列表
+    vegetation_band7_00_00 = []
+    vegetation_band14_00_00 = []
+
+    # 定义疑似高温火点列表，初始化为白色 (255)
+    suspicious_hotspots = np.full_like(band7_00_00_temperature, 255, dtype=np.uint8)
+
+    # 遍历每个像元
+    for row in range(len(band7_00_00_temperature)):
+        for col in range(len(band7_00_00_temperature[0])):
+            if water_cloud_sun_mask_image[row][col] == 255:
+
+                # 获取当前像元的温度数据
+                band7_00_00_temperature_current = band7_00_00_temperature[row][col]
+                band14_00_00_temperature_current = band14_00_00_temperature[row][col]
+
+                # 检查白天和夜晚的温度阈值条件
+                if band7_00_00_temperature_current > day_threshold or band7_00_00_temperature_current > night_threshold :
+                    
+                    # 初始化邻域扩展步长
+                    expansion = background_expansion
+                    
+                    # 初始化晴空植被像元数量
+                    clear_vegetation_count = 0
+                    
+                    # 开始扩展邻域，直到满足晴空植被像元数量条件
+                    while clear_vegetation_count < (expansion * expansion * 0.2):
+                        
+                        # 获取邻域像元数据
+                        neighborhood_band7_00_00 = band7_00_00_temperature[
+                            row - expansion // 2 : row + expansion // 2 + 1,
+                            col - expansion // 2 : col + expansion // 2 + 1,
+                        ]
+                           
+                        # 计算左上角和右上角的行列索引
+                        top_left_row = max(row - expansion // 2, 0)
+                        top_left_col = max(col - expansion // 2, 0)
+                        bottom_right_row = min(row + expansion // 2, 6001 - 1)
+                        bottom_right_col = min(col + expansion // 2 + 1, 6001)
+
+                        # 遍历每个像元
+                        for row in range(top_left_row, +bottom_right_row + 1):
+                            for col in range(top_left_col, bottom_right_col):
+                                # 获取当前像元的植被指数值
+                                ndvi_value = ndvi_data[row][col]
+
+                                # 判断是否为晴空植被像元
+                                if ndvi_value > vegetation_threshold:
+                                    # 如果是晴空植被像元，将其添加到晴空植被像元数量中
+                                    clear_vegetation_count += 1
+
+                                    # 计算背景晴空植被像元band7和band14亮温
+                                    vegetation_band7_00_00.append(band7_00_00_temperature[row][col])
+                                    vegetation_band14_00_00.append(band14_00_00_temperature[row][col])
+
+                        # 如果还不满足条件，增加邻域扩展步长
+                        if clear_vegetation_count < (expansion * expansion * 0.2):
+                            expansion += 2  # 可以按需调整步长，例如+2
+
+                        # 如果扩展步长超过最大值（例如，19x19），则退出循环    
+                        if expansion > 19:
+                            break 
+
+                    # 计算温度差和平均温度差
+                    delta_B7_14 = band7_00_00_temperature_current - band14_00_00_temperature_current
+                    delta_B7_11_bg = np.array(vegetation_band7_00_00) - np.array(vegetation_band14_00_00)
+
+                    # 检查疑似高温火点条件                   
+                    if band7_00_00_temperature_current > np.mean(vegetation_band7_00_00) + 10 and  delta_B7_14 > np.mean(delta_B7_11_bg) + 8:
+                        # 将当前像元标记为疑似高温火点
+                        suspicious_hotspots[row][col] = np.array( 0, dtype=np.uint8)  # 黑色                  
+
+    suspicious_hotspots= np.array(suspicious_hotspots, dtype=np.uint8)
+
+    # 保存图像到本地
+    cv2.imwrite("E:/Vscode_code/threshold_adapt_fireimage/suspicious_hotspots.tif", suspicious_hotspots)
+
+     # Open target TIFF for update
+    target_dataset = gdal.Open("E:/Vscode_code/threshold_adapt_fireimage/suspicious_hotspots.tif", gdal.GA_Update) 
+    dataset_00_geo_transform = dataset_00.GetGeoTransform()
+
+     # Apply the source geo-transform to the target TIFF
+    target_dataset.SetGeoTransform(dataset_00_geo_transform)
+               
+
+    # Close the datasets to save changes
+    dataset_00 = None
+    target_dataset = None
+    
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main()