Satellite images are advantageous to large-area and repetitive observation, and therefore, are gradually adopted in the tasks of resource monitoring. Their analysis and presentation for the present are important to resource monitoring, but the most widely used SPOT satellite images can only show the image of false color, not the true color that is familiar to human eyes. This study attempts to develop a back propagation neural network method to transform a SPOT false color image into a SPOT simulate true color image.
This study consists of three steps to test the neural network method：（1）in the same period, use the Landsat false color image and the Landsat true color image in the training of neural network to get a weighting,（2）put the SPOT false image into the neural network of input layer, and create a simulated SPOT true color image,（3）use the SPOT false image of different periods with Landsat image to test the result.
The above results indicate that by visualization and mathematical testing, the presented colors are similar to Landsat nature color and their correlation coefficients are greater than 0.90. It means that this experiment is workable when we try to use simulated neural network to produce true color images. The application of SPOT true color image is certainty efficient.
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