In this project, we aimed to apply different filters and masks to a spatial subset of a satellite image. We use Landsat 8 meta-data file to obtain the image.
Then some remote sensing indices have been created using the “band math” option in the ENVI program.
After creating the desired indicators, we will use them to specify each of the complications and coverages. We need to separate the five desired land coverages using indicators and each of the bands. These five land covers are:
- Dense vegetation
- sparse vegetation
- Urban coverage
- Sea coverage
- Cloud coverage
To obtain each of these land covers, we act as follows:
- Dense vegetation: we use the NDVI index and mask this image by giving the appropriate value.
- Dense vegetation cover: to mask this cover, we also use the NDVI index, which represents the vegetation cover.
- Urban coverage: To obtain urban coverage, we use the NDBI index and its appropriate values.
- Sea coverage: Blue areas are obtained using the NDWI index.
- Cloud coverage: Cloud area is also obtained by using one of the bands called Quality.
Noise removal and edge extraction in panchromatic images
In the first step, we loaded the panchromatic image from the QuickBird sensor into Envi classic software. The panchromatic image has the highest spatial resolution among the spectral bands. For this reason, it is considered as the best band for spatial analysis.
After adding this band from the QuickBird satellite in the software, noises are observed on its surface. To eliminate the noise in the image, we must use low-pass filters.
In this part of the exercise, we selected a low-pass Gaussian filter from the Enhancement section of ENVI classic to remove noises.
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