On the use of synthetic images for change detection accuracy assessment

Land cover change detection is the major goal in multitemporal remote sensing studies. It is well known that remotely-sensed images of the same area acquired on different dates tend to be affected by radiometric differences and registration problems. These influences are considered as noise in the process and may induce the user to both: signalling false changes and masking real surface changes. The difference image produced by subtracting two co-registered images is a standard initial step in change detection algorithms. This image naturally appears to be noisier than the original ones and has at least two populations: i) the noise-like and ii) the real changes. The problem that arises is how to discriminate them. There are several approaches to perform change detection reported in the literature and some studies have employed synthetic images. By using synthetic images, the accuracy assessment of specific algorithm can be done more accurately. The question at this point is: what is the acceptable noise level to be added on the synthetic images to simulate a real problem? This paper attempts to answer this question by suggesting values of SNR (signal-to-noise ratio) obtained from experiments performed on TM-Landsat-5 and CCD-CBERS-2B images.