Since you are trying to discover structure in the images it's better to work in grayscale.
This is a very nice case where the court appears to be a nice rectangle, but in general, courts might come in different sizes and orientations. Also, white lines on green background is not a general rule, consider for example Roland Garos.
Having said this, you can try thresholding your image (you might have to use adaptive thresholding for this) to get something like this:
You can then go through the image creating chains of pixels that are 4 or 8 connected
Since you are looking for lines, you can get each of the chains and fit a line to it.
At this point, the "interesting pixels" in your image have been transformed to a set of line models and some of them will be intersecting. You can now apply simple rules to discover which sets of lines are intersecting (here is a good example ).
To discover the set of intersecting lines that make up a tennis court you would search for (this is an example) "A set of 9 lines where 4 are intersecting at right angles, two more parallel to the long lines intersecting them at 1/8 of the width of the field, two more parallel to the short sides and lying about 1/5 from the beginning of the court and a third running in the middle of the court and intersecting the two shorter ones"...In other words, you will have to express the tennis court as a set of intersecting lines with relative positioning to each other (so that your expression is free of the absolute dimensions).
For a very good application of the above technique you can have a look at this paper that outlines the recognition of shotcodes in an image.
For the theoretical background and more information about these techniques you can refer to the excellent "Digital Image Processing With Matlab" and specifically, chapters 10-13 which are the most relevant to your problem
I hope this helps.