Effect of Modulation Transfer Function on high spatial resolution remote sensing imagery segmentation quality

The Modulation Transfer Function (MTF) is a widely used parameter to assess the quality of an imaging system. For the end user, the system MTF can be used to compare the intrinsic quality of imagery from various sources as well as analytically equalize the sharpness of multiple images from different sensors. However, due to the vibration during the satellite launch or some change in material properties, the MTF characteristic may change. As a result, imagery quality may change. However, Current researches mostly lie in how to measure MTF, and don't detailedly analyze the influence of MTF on imagery quality. Therefore, a lot of potential research work is desirable to discuss the relation between MTF and imagery quality. With regard to high spatial resolution remote sensing imagery, MTF mainly affects edge sharpness of imagery. And edge sharpness of imagery can affect imagery segmentation quality. So, the goal of this study is to analyze the effect of MTF on high spatial resolution remote sensing imagery segmentation quality. The paper firstly introduced the algorithm and steps of measuring MTF value based on high spatial resolution remote sensing imagery. Next, a typical imagery, which was derived from an acquisition over Florida, was selected for calculating MTF. In order to simulate different MTF values, the Gaussian PSF (Point Spread Function) was artificially added into the selected imagery. As a result, we can acquire a series of images, which had different MTF values. Then, the images were segmented by the object-based image analyst tool, such as eCongniton Developer 8.64, Feature Analyst, ENVI FX, et al. Finally, we compared the different segmentation quality by the methods of the segmentation accuracy assessment. The Area-Fit-Index (AFI) and Offspring-Loyalty (OL) were used to assess the segmentation accuracy of the images. The paper randomly selected 410 segmented polygons for assessing segmentation accuracy. The results of AFI and OL showed that the segmentation accuracy was lower while MTF value was lower. While MTF dropped still further, the AFI became negative number, −0.04. This showed that the edge profiles of the segmented objects mostly exceeded the edge profiles of the reference objects. The research also demonstrated that different land types had different segmentation quality on the condition of the same MTF value.