@article{Jun-HakLee2016,
title = {An Individual Tree-Based Automated Registration of Aerial Images to Lidar Data in a Forested Area},
author = {Jun-Hak Lee and Gregory Biging and Joshua Fisher},
doi = {https://doi.org/10.1016/S0099-1112(16)30121-5},
year = {2016},
date = {2016-08-01},
journal = {Photogrammetric Engineering & Remote Sensing},
volume = {82},
number = {9},
pages = {699-701},
abstract = {In this paper, we demonstrate an approach to align aerial images to airborne lidar data by using common object features (tree tops) from both data sets under the condition that conventional correlation-based approaches are challenging due to the fact that the spatial pattern of pixel gray-scale values in aerial images hardly exist in lidar data. We extracted tree tops by using an image processing technique called extended-maxima transformation from both aerial images and lidar data. Our approach was tested at the Angelo Coast Range Reserve on the South Fork Eel River forests in Mendocino County, California. Although the aerial images were acquired simultaneously with the lidar data, the images had only approximate exposure point locations and average flight elevation information, which mimicked the condition of limited information availability about the aerial images. Our results showed that this approach enabled us to align aerial images to airborne lidar data at the single-tree level with reasonable accuracy. With a local transformation model (piecewise linear model), the rmse and the median absolute deviation (mad) of the registration were 9.2 pixels (2.3 meters) and 6.8 pixels (1.41 meters), respectively. We expect our approach to be applicable to fine scale change detection for forest ecosystems and may serve to extract detailed forest biophysical parameters.
In this paper, we demonstrate an approach to align aerial images to airborne lidar data by using common object features (tree tops) from both data sets under the condition that conventional correlation-based approaches are challenging due to the fact that the spatial pattern of pixel gray-scale values in aerial images hardly exist in lidar data. We extracted tree tops by using an image processing technique called extended-maxima transformation from both aerial images and lidar data. Our approach was tested at the Angelo Coast Range Reserve on the South Fork Eel River forests in Mendocino County, California. Although the aerial images were acquired simultaneously with the lidar data, the images had only approximate exposure point locations and average flight elevation information, which mimicked the condition of limited information availability about the aerial images. Our results showed that this approach enabled us to align aerial images to airborne lidar data at the single-tree level with reasonable accuracy. With a local transformation model (piecewise linear model), the rmse and the median absolute deviation (mad) of the registration were 9.2 pixels (2.3 meters) and 6.8 pixels (1.41 meters), respectively. We expect our approach to be applicable to fine scale change detection for forest ecosystems and may serve to extract detailed forest biophysical parameters.
