The Segmentation Fusion Method On10 Multi-Sensors
This addresses the challenge of fusing multi-sensor, multi-date remotely sensed images for applications like remote sensing, though it appears incremental as it builds on existing fusion techniques.
The study tackled the problem of undesirable spectral effects in multi-sensor image fusion by proposing a new Segmentation Fusion method, which was tested on 10 multi-sensor images and compared with other techniques using spatial and spectral metrics to quantitatively assess quality and information improvement.
The most significant problem may be undesirable effects for the spectral signatures of fused images as well as the benefits of using fused images mostly compared to their source images were acquired at the same time by one sensor. They may or may not be suitable for the fusion of other images. It becomes therefore increasingly important to investigate techniques that allow multi-sensor, multi-date image fusion to make final conclusions can be drawn on the most suitable method of fusion. So, In this study we present a new method Segmentation Fusion method (SF) for remotely sensed images is presented by considering the physical characteristics of sensors, which uses a feature level processing paradigm. In a particularly, attempts to test the proposed method performance on 10 multi-sensor images and comparing it with different fusion techniques for estimating the quality and degree of information improvement quantitatively by using various spatial and spectral metrics.