Additional research effort is needed on SAR-optical fusion for improved detection and mapping of degraded forests [94].
This paper mainly addresses the problem of multipolar Synthetic Aperture Radar (SAR) and colorful optical images fusion by regarding them as multichannel images.
SAR-optical data fusion may also improve the discrimination and mapping of degraded forests.
Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data.
Further R&D should focus on the use of archive data for determining baseline carbon stocks [57], and tracking of secondary forest dynamics using SAR-optical data fusion in a range of forest types and with varied disturbance history [29, 59].
This paper focuses on hardware/software implementation and flight results relevant to a multi-sensor obstacle detection and tracking system based on radar/electro-optical (EO) data fusion.
The reliability and transferability of LiDAR-based retrieval algorithms (using next generation LiDARs), SAR-based methods [84, 85] and data fusion (SAR-Optical, SAR-Optical-LiDAR, [10]) approaches should be investigated in the context of estimating change in degraded and regenerating forests.
Therefore, image fusion of optical images after the operation and finishing can be obtained as formula (7) as shown in the optical image vector I O. left{begin{array}{l}{I}_{mathrm{O}}={displaystyle {int}_0^nfleft x,y,zright df}hfill {I}_{mathrm{O}}=frac{I_0}{2delambdambda } ln left[{R}_{mathrm{WV}}+{displaystyle sum_{i=1}^n{R}_{mathrm{WV}}(i }right]hfill end{array}right.
Optical micrographs of the fusion zone of the (a) Mg side and (b) Al side.
The fusion of optical and microwave data, whenever available, has been very useful for increasing the accuracy of the classification.
Figure 1a,b shows optical micrographs of the fusion zone, the Mg side and Al side, respectively.
