How can contrasts within satellite images be identified using Python's OpenCV when the images are combined?

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There are several ways to identify contrasts within satellite images using Python's OpenCV when the images are combined:

1. Use image thresholding: Thresholding is a simple method in which pixels above or below a certain threshold are set to a maximum or minimum value respectively. This can effectively separate bright and dark areas within the image.

2. Use image segmentation: Image segmentation is a more complex method that involves dividing an image into different regions based on intensity, texture, color, etc. This can be done using various algorithms such as region growing, watershed, etc.

3. Use edge detection: Edge detection involves finding the boundaries of objects within an image. This can be done using various algorithms such as Canny edge detection, Sobel operator, etc.

4. Use histogram equalization: Histogram equalization is a method that enhances the contrast of an image by adjusting the intensity levels of the pixels. This can be useful when the contrast between the object of interest and the background is low.

Overall, the choice of method depends on the specific application and the characteristics of the images being processed.