Fusion of Multispectral LiDAR, Hyperspectral, and RGB Data for Urban Land Cover Classification | IEEE Journals & Magazine | IEEE Xplore

Fusion of Multispectral LiDAR, Hyperspectral, and RGB Data for Urban Land Cover Classification


Abstract:

With the increasing importance of monitoring urban areas, the question arises which sensors are best suited to solve the corresponding challenges. This letter proposes no...Show More

Abstract:

With the increasing importance of monitoring urban areas, the question arises which sensors are best suited to solve the corresponding challenges. This letter proposes novel node tests within the random forest (RF) framework, which allows them to apply them to optical RGB images, hyperspectral images, and light detection and ranging (LiDAR) data, either individually or in combination. This does not only allow to derive accurate classification results for many relevant urban classes without preprocessing or feature extraction but also provides insights into which sensor offers the most meaningful data to solve the given classification task. The achieved results on a public benchmark data set are superior to results obtained by deep learning approaches despite being based on only a fraction of training samples.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 18, Issue: 2, February 2021)
Page(s): 366 - 370
Date of Publication: 24 February 2020

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