Towards Multi-source Remote Sensing Information Fast Processing And Automatic Lithological Information Extraction

Remote sensing data from space-and air-borne sensors can be effectively used for lithological classification and mapping, especially in areas of high outcrop density in arid regions. With the rapid advances in sensor systems, remote sensing data are now available with high spatial, spectral, temporal, and radiometric resolution. Lithological units generally have distinct responses in different remote sensing datasets, hence integration of data from multiple remote sensors, including air-borne geophysical sensors, can potentially provide enhanced information about lithologies and facilitate more accurate and reliable lithological mapping. However, the integration of multi-source remote sensing datasets to extract lithological information is not straight forward, and requires rigorous pre-and post-processing. Firstly, co-registration of different remote sensing images is a pre-requisite before applying integration and classification algorithms. However, conventional manual co-registration is cumbersome especially with large volumes of remote sensing data. Automated co-registration, on the other hand, requires addressing complex problems such as varying illuminations and resolutions of the images, different perspectives and local deformations within images, etc. Secondly, vegetation cover is an impediment to lithological mapping from remote sensing data, and, in order to enhance the underlying geological information in such terrains, it is desirable to suppress the reflectance component of vegetation. Thirdly, supervised classification algorithms are conventionally used for lithological classifications, but classifiers such as maximal likelihood, neural networks etc. are not capable of learning complex patterns in high dimensional feature spaces, which is a critical characteristic of multi-source datasets.This thesis aims to provide new solutions to the above issues by integrating methods from computer-vision and machine-learning domains for automatic lithological information extraction from multi-source remote sensed datasets (take optical remotely sensed datasets and aeromagnetic dataset for example). Major contributions of this thesis are as follows.(1) A fully automatic and fast non-rigid image registration technique for multi-source imagery is developed. It incorporates the scale invariant feature transform (SIFT) method, the Harris corner detector, wavelet pyramid, cross-matching strategy and triangulated irregular networks (TINs). Experiments with multi-source high and moderate resolution remote sensing images demonstrate the efficiency and the accuracy of the proposed technique.(2) A "masking-forced invariance" algorithm is proposed for the suppression of the vegetation reflectance component in a densely vegetated study area. An evaluation based on comparison with the geological map shows that the forced invariance technique results in significant enhancement of the lithological information in the processed image.(3) Support vector machine (SVM) algorithm is applied to integrate Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) imagery with ASTER-derived digital elevation mode (DEM) and aeromagnetic data to implement automated lithological mapping of the study area. Experiments with other supervised classification techniques such as maximum likelihood classifier, minimum distance classifier and neural network classifer (including BPNN and PNN) show that the SVM provides higher accuracy both in terms of classification of independent test samples as well as similarity with the available bed-rock geological map. Another experiment on AVIRIS hyperspectral data in Cuprite, Nevada, USA also shows the effectiveness of using SVM for lithological classification.(4) A feature selection algorithm based on support vector machine called SVM recursive feature elimination or SVM-RFE is used to find the importance of different input features in SVM-based lithological classification. The experimental results for the above lithological classification case study indicate that the feature subset generated by the SVM-RFE is acceptable in terms of classification accuracy. The accuracy remains almost the same when only 19 features out of 36 are reserved. SVM-RFE not only is proved to be an effective tool to shrink high dimensionality dataset, but also is a useful tool to rank the importance (knowledge) of different inputs.In summary, the main innovations of this thesis are:(1) a SVM-based automatic lithological classification method for multi-source remote sensing datasets, namely, ASTER, DEM and aeromagnetic; then a SVM-RFE based data mining method to shrink data dimensionality and discover knowledge of lithology classification from input datasets. (2) in remote sensing image pre-processing and enhancement procedures, a fast and fully automatic registration technique based on point features for multi-source remote-sensing image; and a "masking-forced invariance" approach for suppression of vegetation in multispectral remote sensing images.