The terrestrial laser scanner is an equipment developed for surveying applications and is also used for many other purposes due to its ability to acquire 3D data quickly. However, before intensity data can be analyzed, it must be processed in order to minimize the edge or border effect, one of the most serious problems of LIDAR's intensity data. Our research has focused on characterizing the edge effect behavior as well as to develop an algorithm to minimize edge effect distortion automatically (IRA). The IRA showed to be effective recovering 35.71% of points distorted by the edge effect, providing significant improvements and promising results for the development of applications based on TLS data intensity to many studies.

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ABSTRACT:Laser scanning technique from airborne and land platforms has been largely used for collecting 3D data in large volumes in the field of geosciences. Furthermore, the laser pulse intensity has been widely exploited to analyze and classify rocks and biomass, and for carbon storage estimation. In general, a laser beam is emitted, collides with targets and only a percentage of emitted beam returns according to intrinsic properties of each target. Also, due interferences and partial collisions, the laser return intensity can be incorrect, introducing serious errors in classification and/or estimation processes. To address this problem and avoid misclassification and estimation errors, we have proposed a new algorithm to correct return intensity for laser scanning sensors. Different case studies have been used to evaluate and validated proposed approach.

ABSTRACT:Laser scanning technique from airborne and land platforms has been largely used for collecting 3D data in large volumes in the field of geosciences. Furthermore, the laser pulse intensity has been widely exploited to analyze and classify rocks and biomass, and for carbon storage estimation. In general, a laser beam is emitted, collides with targets and only a percentage of emitted beam returns according to intrinsic properties of each target. Also, due interferences and partial collisions, the laser return intensity can be incorrect, introducing serious errors in classification and/or estimation processes. To address this problem and avoid misclassification and estimation errors, we have proposed a new algorithm to correct return intensity for laser scanning sensors. Different case studies have been used to evaluate and validated proposed approach.