Landsat 8 波段组合

Landsat8已上线数月,其图像质量惊人。它不仅保留了前代的所有波段,还新增了海岸蓝波段和卷云波段等。本文详细介绍了Landsat8的波段组合及与Landsat7的对比。
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Landsat 8 has been online for a couple of months now, and the images look incredible. While all of the bands from previous Landsat missions are still incorporated, there are a couple of new ones, such as the coastal blue band water penetration/aerosol detection and the cirrus cloud band for cloud masking and other applications. Here’s a rundown of some common band combinations applied to Landsat 8, displayed as a red, green, blue (RGB):

Natural Color 4 3 2
False Color (urban) 7 6 4
Color Infrared (vegetation) 5 4 3
Agriculture 6 5 2
Atmospheric Penetration 7 6 5
Healthy Vegetation 5 6 2
Land/Water 5 6 4
Natural With Atmospheric Removal 7 5 3
Shortwave Infrared 7 5 4
Vegetation Analysis 6 5 4

Here’s how the new bands from Landsat 8 line up with Landsat 7:

Landsat 7

Landsat 8

Band Name Bandwidth (µm) Resolution (m) Band Name Bandwidth (µm) Resolution (m)
Band 1 Coastal

0.43 – 0.45

30

Band 1 Blue

0.45 – 0.52

30

Band 2 Blue

0.45 – 0.51

30

Band 2 Green

0.52 – 0.60

30

Band 3 Green

0.53 – 0.59

30

Band 3 Red

0.63 – 0.69

30

Band 4 Red

0.64 – 0.67

30

Band 4 NIR

0.77 – 0.90

30

Band 5 NIR

0.85 – 0.88

30

Band 5 SWIR 1

1.55 – 1.75

30

Band 6 SWIR 1

1.57 – 1.65

30

Band 7 SWIR 2

2.09 – 2.35

30

Band 7 SWIR 2

2.11 – 2.29

30

Band 8 Pan

0.52 – 0.90

15

Band 8 Pan

0.50 – 0.68

15

Band 9 Cirrus

1.36 – 1.38

30

Band 6 TIR

10.40 – 12.50

30/60

Band 10 TIRS 1

10.6 – 11.19

100

Band 11 TIRS 2

11.5 – 12.51

100

For the most part, the bands line up with what we’re used to, with some minor tweaking of the spectral ranges. The thermal infrared band from Landsat 7 is now split into two bands for Landsat 8. Whereas before you had one thermal band that was acquired at 60 m resolution (and resampled to 30 m) now you have increased spectral resolution at the cost of spatial resolution. It wouldn’t be remote sensing without tradeoffs, right?

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Stable Diffusion 3.5 (SD 3.5) 是由 Stability AI 推出的新一代文本到图像生成模型,相比 3.0 版本,它提升了图像质量、运行速度和硬件效率

### Landsat 8 波段组合图像处理方法 Landsat 8 卫星提供了多个波段的数据,这些波段可以用于生成不同的图像组合来突出特定的地物特征或现象。常见的波段组合包括真彩色、假彩色以及植被指数等。 #### 常见的波段组合及其用途 1. **真彩色 (True Color)** 使用 B4(红色)、B3(绿色)和 B2(蓝色)三个波段合成图像,这种组合最接近人类视觉感知的颜色效果[^1]。 ```python from osgeo import gdal # 打开各波段文件 red_band = gdal.Open('path_to_B4.tif') green_band = gdal.Open('path_to_B3.tif') blue_band = gdal.Open('path_to_B2.tif') # 创建RGB合成图 driver = gdal.GetDriverByName('GTiff') rgb_image = driver.Create('true_color.tif', red_band.RasterXSize, red_band.RasterYSize, 3, gdal.GDT_Byte) # 设置地理变换参数 geo_transform = red_band.GetGeoTransform() projection = red_band.GetProjection() rgb_image.SetGeoTransform(geo_transform) rgb_image.SetProjection(projection) # 将波段写入到多波段TIFF中 rgb_image.GetRasterBand(1).WriteArray(red_band.ReadAsArray()) rgb_image.GetRasterBand(2).WriteArray(green_band.ReadAsArray()) rgb_image.GetRasterBand(3).WriteArray(blue_band.ReadAsArray()) # 关闭数据集 rgb_image.FlushCache() ``` 2. **假彩色 (False Color)** 使用 B5(近红外)、B4(红色)和 B3(绿色)三个波段合成图像,这种组合常用于增强植被覆盖度的效果,因为近红外波段对植被反射率敏感。 ```python nir_band = gdal.Open('path_to_B5.tif') # 近红外波段 red_band = gdal.Open('path_to_B4.tif') # 红色波段 green_band = gdal.Open('path_to_B3.tif') # 绿色波段 false_color = driver.Create('false_color.tif', red_band.RasterXSize, red_band.RasterYSize, 3, gdal.GDT_UInt16) false_color.SetGeoTransform(geo_transform) false_color.SetProjection(projection) false_color.GetRasterBand(1).WriteArray(nir_band.ReadAsArray()) # NIR -> Red channel false_color.GetRasterBand(2).WriteArray(red_band.ReadAsArray()) # Red -> Green channel false_color.GetRasterBand(3).WriteArray(green_band.ReadAsArray()) # Green -> Blue channel false_color.FlushCache() ``` 3. **NDVI 计算 (Normalized Difference Vegetation Index)** NDVI 是一种常用的植被指数,通过计算 B5(近红外)和 B4(红光)之间的差异来评估植被健康状况。 ```python import numpy as np nir_data = nir_band.ReadAsArray().astype(float) red_data = red_band.ReadAsArray().astype(float) ndvi = (nir_data - red_data) / (nir_data + red_data) # 输出NDVI结果为TIFF文件 ndvi_driver = gdal.GetDriverByName('GTiff') ndvi_output = ndvi_driver.Create('ndvi.tif', red_band.RasterXSize, red_band.RasterYSize, 1, gdal.GDT_Float32) ndvi_output.SetGeoTransform(geo_transform) ndvi_output.SetProjection(projection) ndvi_output.GetRasterBand(1).WriteArray(ndvi) ndvi_output.FlushCache() ``` 以上展示了如何利用 GDAL 工具库读取 Landsat 8 的单波段数据,并将其合成为 RGB 图像或者计算 NDVI 指数的过程。 ---
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