Geology topics

C. Huang

Publications and source records attributed to C. Huang.

6 recordsLinked to original sources

USGS2010

A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

International Journal of Remote Sensing↗

USGS2010

An assessment of support vector machines for land cover classification

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

International Journal of Remote Sensing↗

USGS2009

Development of time series stacks of Landsat images for reconstructing forest disturbance history

Forest dynamics is highly relevant to a broad range of earth science studies, many of which have geographic coverage ranging from regional to global scales. While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972, large quantities of Landsat images will need to be analysed for studies at regional to global scales. This will not only require effective change detection algorithms, but also highly automated, high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency, a status called imagery-ready-to-use (IRU). This paper describes a streamlined approach for producing IRU quality Landsat time series stacks (LTSS). This approach consists of an image selection protocol, high level preprocessing algorithms and IRU quality verification procedures. The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy. These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System (LEDAPS) designed for processing large quantities of Landsat images. Some characteristics of the LTSS developed using this approach are discussed.

International Journal of Digital Earth↗

USGS2002

Impact of sensor's point spread function on land cover characterization: Assessment and deconvolution

Measured and modeled point spread functions (PSF) of sensor systems indicate that a significant portion of the recorded signal of each pixel of a satellite image originates from outside the area represented by that pixel. This hinders the ability to derive surface information from satellite images on a per-pixel basis. In this study, the impact of the PSF of the Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m bands was assessed using four images representing different landscapes. Experimental results showed that though differences between pixels derived with and without PSF effects were small on the average, the PSF generally brightened dark objects and darkened bright objects. This impact of the PSF lowered the performance of a support vector machine (SVM) classifier by 5.4% in overall accuracy and increased the overall root mean square error (RMSE) by 2.4% in estimating subpixel percent land cover. An inversion method based on the known PSF model reduced the signals originating from surrounding areas by as much as 53%. This method differs from traditional PSF inversion deconvolution methods in that the PSF was adjusted with lower weighting factors for signals originating from neighboring pixels than those specified by the PSF model. By using this deconvolution method, the lost classification accuracy due to residual impact of PSF effects was reduced to only 1.66% in overall accuracy. The increase in the RMSE of estimated subpixel land cover proportions due to the residual impact of PSF effects was reduced to 0.64%. Spatial aggregation also effectively reduced the errors in estimated land cover proportion images. About 50% of the estimation errors were removed after applying the deconvolution method and aggregating derived proportion images to twice their dimensional pixel size. ?? 2002 Elsevier Science Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2001

Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting

Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS1996

Mapping the neutralizing epitopes on the glycoprotein of infectious haematopoietic necrosis virus, a fish rhabdovirus

Twelve neutralizing monoclonal antibodies (MAbs) against the fish rhabdovirus, infectious haematopoietic necrosis virus (IHNV), were used to select 20 MAb escape mutants. The nucleotide sequence of the entire glycoprotein (G) gene was determined for six mutants representing differing cross-neutralization patterns and each had a single nucleotide change leading to a single amino acid substitution within one of three regions of the protein. These data were used to design nested PCR primers to amplify portions of the G gene of the 14 remaining mutants. When the PCR products from these mutants were sequenced, they also had single nucleotide substitutions coding for amino acid substitutions at the same, or nearby, locations. Of the 20 mutants for which all or part of the glycoprotein gene was sequenced, two MAbs selected mutants with substitutions at amino acids 230-231 (antigenic site I) and the remaining MAbs selected mutants with substitutions at amino acids 272-276 (antigenic site II). Two MAbs that selected mutants mapping to amino acids 272-276, selected other mutants that mapped to amino acids 78-81, raising the possibility that this portion of the N terminus of the protein was part of a discontinuous epitope defining antigenic site II. CLUSTAL alignment of the glycoproteins of rabies virus, vesicular stomatitis virus and IHNV revealed similarities in the location of the neutralizing epitopes and a high degree of conservation among cysteine residues, indicating that the glycoproteins of three different genera of animal rhabdoviruses may share a similar three-dimensional structure in spite of extensive sequence divergence.

Journal of General Virology↗