feat(utils): 添加地表反射率转RGB图像功能.

DATA_SuPER/DEM_SuPER.py

@@ -139,7 +139,7 @@ def process_granule(
     name: str,
     roi: list,
     clip=True,
-    tile_id: str = None,
+    tile_id: str = "",
 ) -> bool:
     """
     读取解压并重命名处理后的指定类型 NASADEM 数据并进行预处理, 包括读取, 裁剪, 镶嵌, 并对坡度坡向进行缩放
@@ -215,12 +215,10 @@ def main(region: list, asset_name: list, tile_id: str):
     os.makedirs(unzip_dir, exist_ok=True)
     os.makedirs(output_dir, exist_ok=True)
     results_urls_file = f"{output_root_dir}\\NASADEM_{tile_id}_results_urls.json"
-    if not os.path.isfile(results_urls_file):
+    # 默认覆盖上一次检索记录
-        results_urls = earthdata_search(asset_name, roi=bbox)
+    results_urls = earthdata_search(asset_name, roi=bbox)
-        with open(results_urls_file, "w") as f:
+    with open(results_urls_file, "w") as f:
-            json.dump(results_urls, f)
+        json.dump(results_urls, f)
-    else:
-        results_urls = json.load(open(results_urls_file))
     # 构造待解压的文件列表
     zip_file_list = [os.path.join(download_dir, os.path.basename(result[0])) for result in results_urls]
 

README.md

@@ -59,7 +59,7 @@ envs_dirs:
   - D:\program\miniforge3\envs
   - 其他路径地址(可选，创建虚拟环境时将会按照顺序查找)
 channels:
-  - defaults
+  - conda-forge
 show_channel_urls: true
 channel_alias: https://mirrors.tuna.tsinghua.edu.cn/anaconda
 default_channels:
@@ -117,6 +117,13 @@ mamba env create -f setup/environment.yml
 mamba activate lpdaac
 ```
 
+- 导出当前虚拟环境
+
+```sh
+mamba env export > setup/lpdaac.yml
+```
+
+
 ## 3 设计思路
 
 ### 3.1 数据组织

utils/common_utils.py

@@ -5,7 +5,7 @@
 
 -------------------------------------------------------------------------------
 Authors: Hong Xie
-Last Updated: 2025-07-07
+Last Updated: 2025-08-13
 ===============================================================================
 """
 
@@ -506,9 +506,9 @@ def reproject_image(
             target_image.shape[1] == image.shape[1]
             and target_image.shape[2] == image.shape[2]
         ):
-            # 若判断为降尺度/等尺度, 则直接使用 cubic_spline 重采样投影到目标影像
+            # 若判断为降尺度/等尺度, 则直接使用 cubic 重采样投影到目标影像
             image_reprojed = image.rio.reproject_match(
-                target_image, resampling=Resampling.cubic_spline
+                target_image, resampling=Resampling.cubic
             )
         else:
             # print("target_image shape is not match with image shape", image.shape, "to", target_image.shape)
@@ -520,7 +520,7 @@ def reproject_image(
         # 使用 target_crs 进行重投影
         reproject_kwargs = {
             "dst_crs": target_crs,
-            "resampling": Resampling.cubic_spline,
+            "resampling": Resampling.cubic,
         }
         if target_shape is not None:
             reproject_kwargs["shape"] = target_shape
@@ -563,7 +563,7 @@ def mosaic_images(
             tif_list,
             nodata=nodata,
             method=method,
-            resampling=Resampling.cubic_spline,
+            resampling=Resampling.cubic,
         )
         # 将结果重新构建为 xarray 数据集
         # 单张SAR影像直接读取 transform: 233400.0 30.0 0.0 3463020.0 0.0 -30.0

utils/sr2rgb.py

@@ -0,0 +1,97 @@
+"""
+将 COG 格式的 Red, Green, Blue 单波段地表反射率图像合成 RGB 图像
+
+1. 对比度线性拉伸至 0-255;
+2. 合并波段;
+3. 保存为 uint8 格式 RGB 图像;
+"""
+
+import os
+import rasterio as rio
+import numpy as np
+
+def sr2rgb(red_path, green_path, blue_path, output_path):
+    """
+    将红、绿、蓝三个单波段地表反射率图像合成为RGB图像
+    
+    参数:
+        red_path (str): 红色波段文件路径
+        green_path (str): 绿色波段文件路径  
+        blue_path (str): 蓝色波段文件路径
+        output_path (str): 输出RGB图像路径
+    """
+    # 检查文件是否存在
+    for path in [red_path, green_path, blue_path]:
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"文件不存在: {path}")
+    
+    print(f"正在处理文件:")
+    print(f"  红波段: {red_path}")
+    print(f"  绿波段: {green_path}")
+    print(f"  蓝波段: {blue_path}")
+    
+    # 读取三个波段数据
+    with rio.open(red_path) as red_src:
+        red_band = red_src.read(1)
+        profile = red_src.profile
+        print(f"红波段形状: {red_band.shape}, 数据类型: {red_band.dtype}")
+        
+    with rio.open(green_path) as green_src:
+        green_band = green_src.read(1)
+        print(f"绿波段形状: {green_band.shape}, 数据类型: {green_band.dtype}")
+        
+    with rio.open(blue_path) as blue_src:
+        blue_band = blue_src.read(1)
+        print(f"蓝波段形状: {blue_band.shape}, 数据类型: {blue_band.dtype}")
+    
+    # 线性拉伸到0-255范围
+    def stretch_band(band):
+        # 处理NaN值
+        band_no_nan = np.where(np.isnan(band), 0, band)
+        band_min = np.min(band_no_nan)
+        band_max = np.max(band_no_nan)
+        
+        # 避免除零错误
+        if band_max == band_min:
+            stretched = np.zeros_like(band_no_nan, dtype=np.uint8)
+        else:
+            stretched = ((band_no_nan - band_min) / (band_max - band_min) * 255).astype(np.uint8)
+        return stretched
+    
+    red_stretched = stretch_band(red_band)
+    green_stretched = stretch_band(green_band)
+    blue_stretched = stretch_band(blue_band)
+    
+    # 合并三个波段为RGB图像
+    rgb_array = np.dstack((red_stretched, green_stretched, blue_stretched))
+    
+    # 更新元数据
+    profile.update(
+        dtype=rio.uint8,
+        count=3,
+        photometric='RGB',
+        nodata=0  # 设置nodata为0，因为uint8的范围是0-255
+    )
+    
+    # 保存RGB图像
+    with rio.open(output_path, 'w', **profile) as dst:
+        for i in range(3):
+            dst.write(rgb_array[:, :, i], i + 1)
+    
+    print(f"RGB图像已保存到: {output_path}")
+
+
+if __name__ == "__main__":
+    # tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2024\\2024012"
+    # red_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RED.subset.tif")
+    # green_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.GREEN.subset.tif")
+    # blue_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.BLUE.subset.tif")
+    # output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RGB.tif")
+
+    tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2025\\2025011"
+    red_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RED.subset.tif")
+    green_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.GREEN.subset.tif")
+    blue_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.BLUE.subset.tif")
+    output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RGB.tif")
+    
+    sr2rgb(red_path, green_path, blue_path, output_path)