133 Comparison Between First Pulse and Last Pulse Laser
Scanner Data in the Automatic Detection of Buildings
Leena Matikainen, Juha Hyyppä, and Harri Kaartinen
A comparison between first pulse and last pulse laser scanner data in building detection was carried out. The automatic building detection method included region-based segmentation of a laser scanner derived digital surface model and classification of the segments by using the laser scanner data and an aerial ortho-image. Visual and numerical quality evaluations showed that the correctness of the results improved when last pulse data were used instead of first pulse data. According to a pixel-based comparison with a building map, the improvement was about 8 percentage units. The number of classification errors in the surroundings of the buildings decreased as a result of less vegetation. The number of false detections also decreased. These improvements were clearly shown by a building-based quality evaluation, where the inner part and surrounding area of each reference building was investigated and the number of false detections was calculated. For many buildings, the last pulse data with smaller buildings also corresponded better to the reference map, which improved correctness. The completeness of the results decreased slightly (about 2 percentage units according to the pixel-based comparison). One reason for this was the smaller buildings in the last pulse data.
147 Multivariate Image Texture by Multivariate Variogram
for Multispectral Image Classification
Peijun Li, Tao Cheng, and Jiancong Guo
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.
159 A Comparison of Individual Tree and Forest Plot
Height Derived from LiDAR and InSAR
Shengli Huang, Stacey A. Hager, Kerry Q. Halligan, Ian S. Fairweather, Alan K. Swanson, and Robert L. Crabtree
To compare the capability and the accuracy of Light Detection And Ranging (lidar) and Interferometric Synthetic Aperture Radar (INSAR) for the detection and measurement of individual tree heights and forest plot heights, one lidar dataset with nominal spacing of 3 m and one short-wavelength Ku-band INSAR with comparable ground resolution of 3 m were studied. Vegetation heights were based on the subtraction between the bare ground Digital Elevation Models (DEMs) and the canopy Digital Surface Models (DSMs). Two field measurement datasets of isolated individual trees and forest plots were used for validation. Results showed 78 percent and 17 percent of the individual trees was detected on lidar and INSAR data, respectively. General canopy height patterns could be successfully identified through a transect profile for both sensors, however, both sensors consistently underestimated vegetation height. Higher accuracy was obtained for both individual tree level and forest plot level with lidar than that from the Ku-band INSAR. These results indicate that lidar is much better than INSAR for the detection and estimation of tree and forest plot height.
Color Figures (Adobe PDF Format)
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169 A Remote Sensing and GIS-assisted Spatial Decision
Support System for Hazardous Waste Site Monitoring
John R. Jensen, Michael E. Hodgson, Maria Garcia-Quijano, Jungho Im, and Jason A. Tullis
Humans produce large amounts of waste that must be processed or stored so that it does not contaminate the environment. When hazardous wastes are stored, waste site monitoring is typically conducted in situ which can lead to a serious time lag between the onset of a problem and detection. A Remote Sensing and GIS-assisted Spatial Decision Support System for Hazardous Waste Site Monitoring was developed to improve hazardous waste site management. The system was designed to be recursive, flexible, and integrative. It is recursive because the system is implemented iteratively until the risk assessment subsystem determines that an event is no longer a problem to the surrounding human population or to the environment. It is flexible in that it can be adapted to monitor a variety of hazardous waste sites. The system is integrative because it incorporates a number of different data types and sources (e.g., multispectral and lidar remote sensor data, numerous type of thematic information, and production rules), modules, and human expert knowledge of the hazardous waste sites. The system was developed for monitoring hazardous wastes on the Savannah River National Laboratory near Aiken, South Carolina.
179 Pre-visible Detection of Grub Feeding in Turfgrass
using Remote Sensing
Randy M. Hamilton, Rick E. Foster, Timothy J. Gibb, Christian J. Johannsen, and Judith B. Santini
Japanese beetle grubs (Popillia japonica Newman) are rootfeeding pests of turfgrass in the Midwest and eastern United States causing millions of dollars of damage annually. To reduce unnecessary pesticide output by applying only where needed, turfgrass managers need a practical, noninvasive method to locate patchy infestations before unsightly damage has occurred. Spectrometer data and multispectral aerial imagery were evaluated for detecting pre-visible symptoms of grub damage in turfgrass, to facilitate sitespecific grub management. Using spectrometer reflectance data, first derivatives of reflectance, narrow-band vegetation indices, and linear combinations of multiple bands/indices, infested turfgrass plots were distinguished from un-infested plots 10 to16 days before visual differences appeared in the year when visual ratings were conducted. Pre-visible symptoms of grub feeding were not detected using the position of the red edge or edge of the red edge. Results using multispectral imagery were mixed, with early symptoms detected in only one of two years.
193 A Radiometric Aerial Triangulation for the Equalization
of Digital Aerial Images and Orthoimages
Laure Chandelier and Gilles Martinoty
Atmospheric haze variations, temporal differences, atmosphere’s or ground surfaces’ and Bidirectional Reflectance Distribution Function (BRDF), are well-known sources of radiometric heterogeneities in aerial images. It is necessary to correct them for many applications, such as the generation of large seamless mosaics of orthoimages, or land-cover classifications. This contribution describes a method for equalizing digital aerial images based on a parametric, semiempirical radiometric model. The model’s parameters are computed through a global least-squares minimization process, using radiometric tie-points in overlapping areas between images. The method may be called a “radiometric aerial triangulation” since its principle is quite similar to a standard aerial triangulation. It leads to a relative equalization between images, keeping their original contrast and color information. The method has been successfully applied operationally to more than forty projects comprising three to four thousands digital images each, for the creation of orthoimages over France. The results are good as long as atmospheric conditions are favorable and stable.
201 A Robust Approach for Repairing Color Composite
Jun Pan, Mi Wang, Deren Li, and Jonathan Li
The aerial images acquired by Intergraph’s Digital Mapping Camera (DMC) have been normally used for generating photogrammetric products such as Digital Terrain Models (DTM) and orthophotos. Our experience shows that the failure of radiometric correction in postprocessing leads to residual radiometric differences between CCD images, which then affect the quality of the images for further applications. This paper presents a robust approach to repair defects due to the residual radiometric differences. The approach comprises the autolocating for the transition area and seamline, and the subsequent reconstruction for image in radiometry. A hierarchical strategy for auto-locating is employed to reinforce the robustness, and a multi-scale strategy is adopted to optimize the adjustment in order to reduce the effect of the change of features in image during the subsequent reconstruction in radiometry. Experiments are designed for the validation of the proposed algorithm, in which both qualitative and quantitative analyses are applied to evaluate the performance of the algorithm. The algorithm has been embedded into GeoDodging 4.0 software package (Wuda Geoinformatics Co., Ltd., China), which is specialized in image dodging and mosaicking, as a complementary module for the postprocessing of DMC images.