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Sun Dec 21 07:25:07 2014

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Landsat 8 captures Trentino in November 2014

The beautiful days in early November 2014 allowed to get some nice views of the Trentino (Northern Italy) – thanks to Landsat 8 and NASA’s open data policy:

Landsat 8: Northern Italy 1 Nov 2014
Landsat 8: Northern Italy 1 Nov 2014

Trento captured by Landsat8
Trento captured by Landsat8

Landsat 8: San Michele - 1 Nov 2014
Landsat 8: San Michele – 1 Nov 2014

The beauty of the landscape but also the human impact (landscape and condensation trails of airplanes) are clearly visible.

All data were processed in GRASS GIS 7 and pansharpened with i.fusion.hpf written by Nikos Alexandris.

The post Landsat 8 captures Trentino in November 2014 appeared first on GFOSS Blog | GRASS GIS Courses.

Processing Landsat 8 data in GRASS GIS 7: RGB composites and pan sharpening

banner_pansharpening

In our first posting (“Processing Landsat 8 data in GRASS GIS 7: Import and visualization“) we imported a Landsat 8 scene (covering Raleigh, NC, USA). In this exercise we use Landsat data converted to reflectance with i.landsat.toar as shown in the first posting.

Here we will try color balancing and pan-sharpening, i.e. applying the higher resolution panchromatic channel to the color channels, using i.landsat.rgb.

1. Landsat 8 – RGB color balancing: natural color composites

After import, the RGB (bands 4,3,2 for Landsat 8) may look initially less exciting than expected.This is easy to fix by a histogram based auto-balancing of the RGB color tables.

landsat8_rgb_composite_unbalanced

To brighten up the RGB composite, we can use the color balancing tool of GRASS GIS 7:

grass7_landsat_rgb0

As input, we specify the bands 4, 3, and 2:

grass7_landsat_rgb1

Using a “Cropping intensity (upper brightness level)” of 99 (percent), the result look as follows:

landsat10_rgb_composite_autobalance_99percent_crop

For special purposes or under certain atmospheric/ground conditions it may be useful to make use of the functions “Preserve relative colors, adjust brightness only” or “Extend colors to full range of data on each channel” in the “Optional” tab of i.landsat.rgb.

landsat9_rgb_composite_preserve_relative_colors

You will need to experiment since the results depend directly on the image data.

2. Landsat 8 pansharpening

Pansharpening is a technique to merge the higher geometrical pixel resolution of the panchromatic band (Band 8) with the lower resolution color bands (Bands 4, 3, 2).

GRASS GIS 7 offers several methods through the command i.pansharpen.

1) Brovey transform:

landsat8_pansharpen_brovey1

This module runs in multi-core mode parallelized. The management of the resolution (i.e., apply the higher resolution of the panchromatic band) is performed automatically.

landsat8_pansharpen_brovey2

2. IHS transform

Here we select as above the bands in the i.pansharpen interface but use the “ihs” method.

landsat8_pansharpen_ihs1

HINT: If the colors should look odd, then apply i.landsat.rgb to the pan-sharpened bands (see above).

Color-adjusted IHS pansharpening (with “Cropping intensity: strength=99″):

landsat8_pansharpen_ihs_color_adjusted

Comparison of Landsat 8 RGB composite (39m) and IHS pansharpened RGB composite (15m):

landsat8_rgb432_color_adjusted_zoom landsat8_rgb432_pansharpen_ihs_color_adjusted_zoom

3. PCA transform

Here we select as above the bands in the i.pansharpen interface but use the “pca” method.

landsat8_pansharpen_pca1

Likewise other channels may be merged with i.pansharpen, even when originating from different sensors.

3. Conclusions

Overall, the IHS pansharpening method along with auto-balancing of colors appears to perform very well with Landsat 8.

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