Full-waveform airborne laser scanning has shown increasing utility for earth feature extraction through enhanced physical object recognition. Such data provides users with additional physical observables of the earth's surface. This information has the potential to be exploited alongside geometric information to overcome signal inconsistencies between overlapping flightlines and to improve existing segmentation methodologies. However, because the laser signal is influenced by many variables during travel between the sensor and the target, direct use of this information is not recommended without performing echo amplitude normalization as a function of the incidence angle effect. While existing normalization approaches have proven to be valid over planar features, they tend to perform poorly over nonplanar surfaces. This is primarily due to the lack of robust local surface normal estimation. Realizing these shortcomings, this paper proposes a new echo amplitude normalization approach, where each echo's incidence angle is estimated based on illumination direction and local surface orientation. The local surface orientation estimation method computes the normal to an individual point using the minimum number of points. 3-D moment invariants are used to deliver the normal vector using a weighting function. Thereafter, a vector dot product in 3-D space is adopted to check planarity, ensuring robustness. This method is shown to overcome the weaknesses of existing approaches, performing strongly in challenging areas of rough natural terrain, as well as for planar features. Consequently, this method could be adopted in order to compensate incidence angle effects in any laser scanning physical signals for a range of downstream radiometric calibration and point cloud segmentation applications.

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3D models delivered from digital photogrammetric techniques have massively increased and developed to meet the requirements of many applications. The reliability of these models is basically dependent on the data processing cycle and the adopted tool solution in addition to data quality. Agisoft PhotoScan is a professional image-based 3D modelling software, which seeks to create orderly, precise n 3D content from fixed images. It works with arbitrary images those qualified in both controlled and uncontrolled conditions. Following the recommendations of many users all around the globe, Agisoft PhotoScan, has become an important source to generate precise 3D data for different applications. How reliable is this data for accurate 3D modelling applications is the current question that needs an answer. Therefore; in this paper, the performance of the Agisoft PhotoScan software was assessed and analyzed to show the potential of the software for accurate 3D modelling applications. To investigate this, a study was carried out in the University of Baghdad / Al-Jaderia campus using data collected from airborne metric camera with 457m flying height. The Agisoft results show potential according to the research objective and the dataset quality following statistical and validation shape analysis.

Remote sensing data are increasingly being used in digital archaeology for the potential non-invasive detection of archaeological remains. The purpose of this research is to evaluate the capability of standalone (LiDAR and aerial photogrammetry) and integration/fusion remote sensing approaches in improving the prospecting and interpretation of archaeological remains in Cahokia’s Grand Plaza. Cahokia Mounds is an ancient area; it was the largest settlement of the Mississippian culture located in southwestern Illinois, USA. There are a limited number of studies combining LiDAR and aerial photogrammetry to extract archaeological features. This article, therefore, combines LiDAR with photogrammetric data to create new datasets and investigate whether the new data can enhance the detection of archaeological/ demolished structures in comparison to the standalone approaches. The investigations are implemented based on the hillshade, gradient, and sky view factor visual analysis techniques, which have various merits in revealing topographic features. The outcomes of this research illustrate that combining data derived from different sources can not only confirm the detection of remains but can also reveal more remains than standalone approaches. This study demonstrates that the use of combination remote sensing approaches provides archaeologists with another powerful tool for site analysis.