J.R. Harris

Geological analysis of hyperspectral data over southwest Baffin Island: methods for producing spectral maps that relate to variations in surface lithologies

A hyperspectral dataset acquired over a small portion of southwest Baffin Island in 2004 is analyzed for lithological information. Four different approaches are employed to extract spectral information from the imagery, creating “spectral” maps that potentially reflect different rock types. A “hybrid” approach combining elements of simple image enhancement, automatic classification, and spectral knowledge of various minerals was found to be, arguably, the best approach for producing a predictive map of different lithologies and subunits of the main rock types. Hyperspectral remote sensing, given a suitable geologic-biophysical environment, is a useful tool for the geologic mapping of Canada’s North.

A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic

To map Arctic lithology in central Victoria Island, Canada, the relative performance of advanced classifiers (Neural Network (NN), Support Vector Machine (SVM), and Random Forest (RF)) were compared to Maximum Likelihood Classifier (MLC) results using Landsat-7 and Landsat-8 imagery. A ten-repetition cross-validation classification approach was applied. Classification performance was evaluated visually and statistically using the global classification accuracy, producer’s and user’s accuracies for each individual lithological/spectral class, and cross-comparison agreement. The advanced classifiers outperformed MLC, especially when training data were not normally distributed. The Landsat-8 classification results were comparable to Landsat-7 using the advanced classifiers but differences were more pronounced when using MLC. Rescaling the Landsat-8 data from 16 bit to 8 bit substantially increased classification accuracy when MLC was applied but had little impact on results from the advanced classifiers.

Remote predictive mapping of bedrock geology using image classification of Landsat and SPOT data, western Minto Inlier, Victoria Island, Northwest Territories, Canada

Supervised classification (robust classification method) of Landsat-7 and SPOT-5 data was used to analyse the bedrock geology of a part of the western Minto Inlier on Victoria Island, Canada. The robust classification method was used as it provides a series of uncertainty measures for evaluating the classification results. Six bedrock classes including gabbro, basalt, carbonate of the Wynniatt Formation, quartz-arenite of the Kuujjua Formation, evaporite of the Minto Inlet and Killian Formations and Paleozoic carbonate together with six surficial classes including vegetation were defined as the training data set. The resulting classified images derived from the Landsat and SPOT data were very similar in terms of the regional distribution of lithological classes, as reflected by fairly high classification accuracies for both image types. Gabbro and basalt, despite having a similar mineralogical composition, are spectrally distinct throughout most of the study area. Complicating spectral signatures of overlying glacial sediments and/or other overburden materials and spectral similarities between some of the lithologies caused poorer classification in some areas. Generally, the Landsat imagery provided better spectral separability between most of the lithological units than the SPOT imagery. However, in certain areas where the spectral separation between different lithologies is not dependant on the shortwave infrared-2 (SWIR-2; band 7 on Landsat) and/or blue bands (band 1 on Landsat), the SPOT imagery provided a better classification because of higher spatial resolution.

A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics

A modified approach to existing classification procedures, the Robust Classification Method (RCM), is introduced in this study. This algorithm is based on a randomized and repeated sampling of a training dataset in concert with traditional cross-validation of the classification results. A series of predictions (classified maps) and associated uncertainty maps and statistics are produced. The algorithm and associated outputs are discussed and a case study dealing with the classification of surficial materials in an area in Nunavut, Canada (NTS mapsheet 66A) using RCM is presented. The RCM was especially useful for assessing the effects of spectral and spatial variability in the classification process. Specifically, this method provided a majority classification and variability map and confusion statistics to bracket uncertainty in the classification process with respect to statistical (spectral) variability in the training dataset used to perform the classification as well as identifying areas that show spatial variability in classification.

Noise reduction and best band selection techniques for improving classification results using hyperspectral data: application to lithological mapping in Canada's Arctic

Two problems in using hyperspectral data are the effects of noise and redundancy of information (too many channels) on classification results. This paper introduces two methods for dealing with these problems using a hyperspectral dataset over southern Baffin Island in Canadas Arctic region. This paper shows how classification results using matched filtering (MF) are improved on the modified datasets for identifying various lithologies. The noise-reduced dataset produced using the inverse minimum noise fraction (MNF) transform provides the most accurate classifications, followed by the dataset comprising the best bands determined through eigenvector analysis. The supervised approach presented in this paper in which training areas are extracted using a combination of visual analysis of MNF component images, analysis of existing geology, and field observations followed by match filtering produce useful spectral maps to assist in focusing field mapping activities or as stand-alone maps that provide lithologic information even in the absence of field mapping.

Regional Structural Reconnaissance of the Southwestern Grenville Province Using Remotely Sensed Imagery

Recent reconnaissance mapping in the western Grenville Province of Ontario by the Geological Survey of Canada has led to the delineation of discrete lithotectonic domains characterized by different structural styles, lithologies, metamorphic grade and geophysical signatures. This paper investigates the use of satelliteborne remotely sensed data (Landsat MSS, TM, SEASAT-SAR) for the purpose of delineating structural domains in the Grenville Province of Ontario and western Quebec. Image prints at a scale of 1:250,000 were visually interpreted for ductile (bedding, foliation, folds, shears), brittle (fractures, faults) and glacial features. Possible structural domains were delineated from the interpreted ductile features. The interpreted structural domains show a reasonably close correlation with mapped lithotectonic domains in the western Grenville Province of Ontario. The remotely sensed imagery offers a broad synoptic and surprisingly detailed view of the Grenville Province, and is a valuable reconnaissance mapping tool as a precursor to field mapping activities.