Satellite images are widely used in the study of land cover, being used for many tasks such as: the evaluation of urban growth, the monitoring of cultivation areas, the assessment of natural disasters and other applications, to perform these tasks is resorted to to the use of satellite images, they provide a variety of information that depends on the optical instrument carried on board in their payload, the information in the observation satellites, is provided through a data matrix that can be represented by a image, one of the characteristics is the spatial resolution where a pixel in the image corresponds to a coverage in meters on land, with this spatial resolution we can evaluate large tracts of land, among the spatial resolutions, we have the metrics where a pixel in the image corresponds to more than one meter on the ground and the sub meter, where a pixel in the image corresponds e less than one meter on land. With the sub-metric resolution we have a greater detail of the area of interest on land, having the disadvantage that a smaller area of land is evaluated in comparison with the metric resolution. The image provided by the satellite is composed of a set of matrices commonly called spectral bands, characterized by the bands of colors: red, green, blue, we also have the middle and near infrared bands, among others; additionally, the band of the panchromatic is presented, which is the band where the maximum spatial resolution is identified, therefore the image size is of high resolution. A methodology is presented to process the panchromatic satellite images through the use of the GPGPU programming using the MATLAB tool, a test with a high resolution image and with a weight in 1270 Megabits, with a size of 26012 X 25512 pixels, was performed. which was applied an algorithm where it evaluates the value of the pixel analyzing the whole matrix of the image pixel by pixel, the calculation was made in a Core i7 CPU with a processing time of 2.81 hours, with GPGPU programming using a card GTX1050Ti graphics was processed in a time of 1.65 hours, achieving the same result in a shorter time compared to the CPU processing time.