if you interested with python, you can read a good doc. at GeospatialPython.com, here.

and clipraster.py source is here.

The Process

Clipping a raster is a series of simple button clicks in high-end geospatial software packages. In terms of computing, geospatial images are actually very large, multi-dimensional arrays. Remote Sensing at its simplest is performing mathematical operations on these arrays to extract information from the data. Behind the scenes here is what the software is doing (give or take a few steps):

1. Convert the vector shapefile to a matrix which can be used as mask
2. Load the geospatial image into a matrix
3. Throw out any image cells outside of the shapefile extent
4. Set all values outside the shapefile boundary to NODATA (null) values
5. OPTIONAL: Perform a histogram stretch on the image for better visualization
6. Save the resulting image as a new raster.

If you are interested using Python, a good documentation is available at GeospatialPython.com, here.

and clipraster.py source is here.

The Process:

Clipping a raster is a series of simple button clicks in high-end geospatial software packages. In terms of computing, geospatial images are actually very large, multi-dimensional arrays. Remote Sensing at its simplest is performing mathematical operations on these arrays to extract information from the data. Behind the scenes here is what the software is doing (give or take a few steps):

1. Convert the vector shapefile to a matrix which can be used as mask
2. Load the geospatial image into a matrix
3. Throw out any image cells outside of the shapefile extent
4. Set all values outside the shapefile boundary to NODATA (null) values
5. OPTIONAL: Perform a histogram stretch on the image for better visualization
6. Save the resulting image as a new raster.

if you interested with python, you can read a good doc. at GeospatialPython.com, here.

and clipraster.py source is here.

The Process

Clipping a raster is a series of simple button clicks in high-end geospatial software packages. In terms of computing, geospatial images are actually very large, multi-dimensional arrays. Remote Sensing at its simplest is performing mathematical operations on these arrays to extract information from the data. Behind the scenes here is what the software is doing (give or take a few steps):

1. Convert the vector shapefile to a matrix which can be used as mask
2. Load the geospatial image into a matrix
3. Throw out any image cells outside of the shapefile extent
4. Set all values outside the shapefile boundary to NODATA (null) values
5. OPTIONAL: Perform a histogram stretch on the image for better visualization
6. Save the resulting image as a new raster.

i hope it helps you...

if you interested with python, you can read a good doc. at GeospatialPython.com, here.

and clipraster.py source is here.

The Process

Clipping a raster is a series of simple button clicks in high-end geospatial software packages. In terms of computing, geospatial images are actually very large, multi-dimensional arrays. Remote Sensing at its simplest is performing mathematical operations on these arrays to extract information from the data. Behind the scenes here is what the software is doing (give or take a few steps):

1. Convert the vector shapefile to a matrix which can be used as mask
2. Load the geospatial image into a matrix
3. Throw out any image cells outside of the shapefile extent
4. Set all values outside the shapefile boundary to NODATA (null) values
5. OPTIONAL: Perform a histogram stretch on the image for better visualization
6. Save the resulting image as a new raster.