GPU Implementation of Satellite Image Filtering using OpenCL

Institute for Geoinformatics Advanced processing of geospatial data- GPU, Grid and

Cloud computing

Ermias Beyene Tesfamariam 15 July 2010

Objective • Efficient edge detection method for remote

sensing imageries.

• implementation of image filtering on programmable GPU using the openCL language

• Comparison of different algorithms (Sobel vs. Laplacian) for their efficiency and quality

Application

• The HOST executes the code written as usual, using C++.

• The DEVICES execute OpenCL code. • Use specific OpenCL compiler for the CPU & for

the GPU (ATI Stream).

• The OpenCL API has functions to identify devices, compile programs, send and receive information and run OpenCL program on the chosen device.

Application

OpenCL Code: 1 - Create the OpenCL code using OpenCL language;

Host Code: 2 - Create program using C++; 3 - Import the data to be processed; 4 - Use the OpenCL API to transfer data to the devices; 5 - Use the OpenCL API to call executions; 6 - Retrieve processed data.

Main OpenCL API Commands

• Memory allocation via API – clCreateBuffer

• Accessing device memory via API – clEnqueueWriteBuffer

– clEnqueueReadBuffer

Method • Landsat imageries

– Bands with high contrast, e.g. Band 4

• Image Convolution – 3X3 Filtering Mask convolves over the image

• Sobel Algorithm

• Laplacian Algorithm

• 2-D anisotropic measure of the 1st spatial derivative of an image.

Sobel Filter

Sobel filter • consists of two kernels (Masks) which detect

horizontal and vertical changes in an image • The 3x3 Sobel kernels are:

– Horizontal

– Vertical

Sobel filter

Laplacian filter 2-D isotropic measure of the 2nd spatial derivative of an image.

Sobel Filter Output Image

Sobel Filter Output Image

GPU vs CPU performance Sobel Filter

Laplacian Filter

Open Issue

• Applying Image smoothing and contrast enhancement before/during filtering