根据选定波段索引生成RGB影像并导出.mat文件的技术咨询

完整修改代码 #

from osgeo import gdal
import numpy as np
from tqdm_progress import TqdmUpTo
import scipy.io as scio
import matplotlib.pyplot as plt
import h5py
import mat73

def stretch_band(band, lower_percent=2, upper_percent=98):
    # 波段线性拉伸，适配显示范围
    lower = np.percentile(band, lower_percent)
    upper = np.percentile(band, upper_percent)
    band_stretch = np.clip(band, lower, upper)
    band_stretch = (band_stretch - lower) / (upper - lower)
    return band_stretch

def main():
    remove_bands = []
    mat_dict = mat73.loadmat('0042.mat')
   
    for key in mat_dict:
        if type(mat_dict[key]) is np.ndarray:
            image_data = mat_dict[key]  # type: np.ndarray

    cols = image_data.shape[1]
    rows = image_data.shape[0]
    bands = image_data.shape[2]

    image_data = image_data.reshape(cols * rows, bands)
    sel_bands_list = []
    sel_bands_list = mev_sfs(image_data, 3, remove_bands)

    # ----以下为新增逻辑----
    # 若索引为1基（matlab风格），需先转为0基，取消注释下行即可
    # sel_bands_list = [x-1 for x in sel_bands_list]

    # 1. 提取选中波段，恢复三维影像结构
    sel_bands_data = image_data[:, sel_bands_list].reshape(rows, cols, 3)

    # 2. 保存为.mat格式文件
    scio.savemat('selected_bands.mat', {'sel_bands_data': sel_bands_data, 'sel_bands_idx': sel_bands_list})

    # 3. 生成RGB图像
    r = stretch_band(sel_bands_data[:,:,0])
    g = stretch_band(sel_bands_data[:,:,1])
    b = stretch_band(sel_bands_data[:,:,2])
    rgb_img = np.stack([r,g,b], axis=-1)

    plt.figure(figsize=(10,10))
    plt.imshow(rgb_img)
    plt.axis('off')
    # 如需保存RGB图像取消下行注释
    # plt.savefig('rgb_result.png', dpi=300, bbox_inches='tight')
    plt.show()

if __name__ == "__main__":
    main()

逻辑说明 #

• 选中波段存储：通过scio.savemat直接将选中的波段数据和对应索引存入mat文件，可直接在matlab中读取使用
• RGB图像生成：对三个选中波段分别做2%-98%百分位拉伸，消除极值对显示效果的影响，映射到0-1范围后拼接为RGB影像，支持直接显示和本地保存
• 索引适配：如果波段选择算法返回的是matlab风格的1基索引，取消对应行注释转为Python支持的0基索引即可

内容的提问来源于stack exchange，提问作者asif