Kerry Cawse-Nicholson

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Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process and under- or overestimation of this number may lead to incorrect unmixing in unsupervised methods. In this paper, we discuss a new method for determining the intrinsic dimension using recent advances in random matrix theory. This method is(More)
An important application of terrestrial laser scanning is the extraction of tree stem models for diameter at breast height (DBH) assessment and forest inventory. Much work has been done to automate this process using adjacent co-registered lidar scans. Existing studies, however, have focused on pre-registered point clouds obtained from commercial lidar(More)
Terrestrial laser scanning (TLS) has emerged as an effective tool for rapid comprehensive measurement of object structure. Registration of TLS data is an important prerequisite to overcome the limitations of occlusion. However, due to the high dissimilarity of point cloud data collected from disparate viewpoints in the forest environment, adequate(More)
Airborne Light Detection and Ranging (LiDAR) is used in many 3D applications, such as urban planning, city modeling, facility management, and environmental assessments. LiDAR systems generate dense 3D point clouds, which provide a distinct and comprehensive geometrical description of object surfaces. However, the challenge is that most of the applications(More)
Determining the number of spectral endmembers in a hyper-spectral image is an important step in the spectral unmixing process, and under- or overestimation of this number may lead to incorrect unmixing for unsupervised methods. In this paper we discuss a new method for determining the number of endmembers, using recent advances in Random Matrix Theory. This(More)
Studies on real hyperspectral data have shown that spectrally correlated noise may have a negative impact on noise approximation methods and hence on procedures that require accurate noise estimates, for example intrinsic dimension estimation. The exact behavior of this noise and the best method to overcome its effect is not well understood. In this paper(More)
Accurately estimating the noise in a hyperspectral image is necessary for many applications, including noise whitening and dimension reduction, for example. Inaccuracies in the noise estimation may lead to incorrect results in the information extraction from the image. It is known that most real images contain noise that is correlated between spectral(More)
Waveform light detection and ranging (wLiDAR) records the entire backscattered signal of an emitted laser pulse, enabling deeper penetration through tree canopy. However, due to interactions (e.g. reflection, absorption, and transmission) within the canopy, the full proportion of emitted light may not reach the ground, resulting in an attenuated waveform.(More)
Description and quantification of a landscape or scene can be achieved by assessing its spectral and structural properties. Fusion of spectral information from aerial imagery and 3-D structural information from LiDAR point clouds allows us to integrate these two complementary characteristics. However, in any fusion method, alignment of data sets is crucial.(More)
Determining the intrinsic dimension of a hyperspectral image is an important step in the spectral unmixing process, and under- or over-estimation of this number may lead to incorrect unmixing for unsupervised methods. It is known that most real images contain noise that is not i.i.d. across bands, and so methods that assume i.i.d. noise are often avoided.(More)