Performance of entire-spectrum-processing complex-valued neural-network filter to generate digital elevation model in interferometric radar

摘要

Recently we proposed a singular-unit restoration filter based on complex-valued neural networks (CVNN) that deal with spatial spectrum in interferometric synthetic aperture radar. We named it entire-spectrum-processing CVNN (ESP-CVNN) filter. This filter utilizes more neural generalization ability than other conventional methods. It shows a higher performance with a smaller or almost the same processing time. In this paper, we analyze the relationship between its performance and geographic conditions to be processed. Experiments reveal that the ESP-CVNN filter is superior to conventional filters in particular when an observation area has higher density of phase singular points.