Rock unit discrimination on Landsat TM, SIR-C and Radarsat images using spectral and textural information

Abstract

The paper presents results for spectral and textural analysis of the rock units in Landsat Thematic Mapper (TM) images, dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR)-C images, and Cband HH polarization Standard Beam 4 and Extended High Incidence Beam 3 Radarsat images from a study area between California and Arizona, USA. Fractal dimension, lacunarity and grey-level co-occurrence matrix (GLCM) textural feature images were created from the SIR-C and Radarsat images. Fractal dimensions were calculated using a differential box counting method and lacunarity measures were obtained using a new grey-scale lacunarity estimation method for 36 sample images extracted from the SIR-C and Radarsat images. The fractal dimension and lacunarity curves and class signature separability analysis show that, for rock unit discrimination using image textural features in the study area, the SIR-C L-HH image is more suitable than other SIR-C images and Radarsat images, and that co-polarization (HH) generally provides more textural information than cross-polarization (HV) in the study area. The study also shows that lacunarity measures can reveal the scaling properties of radar image textures for rock units. The combination of spectral information from Landsat TM images and textural information from radar images improves the image classification accuracy of rock units in the study area.

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

“…For example, Rowan et al (2004) utilised subtle spectral features relating to variations in vegetation cover to map specific lithological units in the Mordor Complex, Australia, while Harris et al (2005) used a vegetation spectral end-member as a proxy for mapping metagabbroic rocks in southern Baffin Island, Canada. However, if lithology and vegetation are unrelated, or if distinct lithologies exhibit an inherent spectral similarity regardless of vegetation cover, spectral data alone are often insufficient for successful discrimination (Schetselaar et al, 2000;Dong & Leblon, 2004). In such circumstances it may be beneficial to consider ancillary information for the differentiation and mapping of lithological units.…”

“…Numerous studies have assessed the ability to augment the lithological mapping results of spectral-only classifications by incorporating ancillary data such as topographic information (Hutchinson, 1982;Ricchetti, 2000), spectral-derived texture (Chica-Olmo & Abarca-Hernández, 2000; Li et al, 2001) and radar-derived texture (Mather et al, 1998;Dong & Leblon, 2004).…”

The use of lacunarity for visual texture characterization of pre-sliced cooked pork ham surface intensities

“…The results showed that lacunarity scatter plots explicitly characterized the spatial organization of the images and measured space filling capacity and heterogeneity. Any single-value result would be inadequate for characterizing heterogeneous textures, and since lacunarity is scale-dependent, its use at a single scale would be limited compared with the lacunarity measures at different scales (Dong & Leblon, 2004). Lacunarity was smaller when the binary images were nearly translationally invariant, being made of ''diffuse" clumps (structures) separated by smaller empty lacunas (gaps).…”

Derivation of Strike and Dip in Sedimentary Terrain Using 3D Image Interpretation Based on Airborne LiDAR Data

“…In recent decades remote sensing data such as aerial photographs and satellite images have been used for geological mapping (Crósta and Moore 1989;Drury 1993;Dueholm et al 1993;Dong and Leblon 2004;Bedini 2009;Rogge et al 2009;Roy et al 2009; Van der Meer et al 2012;Lato et al 2013). However, in areas with dense vegetation, such photo-geological mapping cannot easily detect subtle geological lineaments or elements on the ground.…”