Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Mapping indicators of riparian vegetation health using IKONOS and Landsat-7 ETM+ image data in Australian tropical savannas

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring environmental health indicators. The objective of this work was to evaluate IKONOS and Landsat-7 ETM+ imagery for mapping riparian vegetation health indicators in tropical savannas for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from IKONOS data were used for estimating percentage canopy cover (r2=0.86). Pan-sharpened IKONOS data were used to map riparian species composition (overall accuracy=55%) and riparian zone width (accuracy within 4 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones, which may be used as riparian environmental health indicators

Remotely sensed data for ecosystem analyses: Combining hierarchy and scene models

Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales; however, a limited range of satellite imaging systems often. constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem; (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.

The influence of coating materials on some properties of alginate beads and survivability of microencapsulated probiotic bacteria

The probiotics, Lactobacillus acidophilus 547, Bifidobacterium bifidum ATCC 1994, and Lactobacillus casei 01, were encapsulated into uncoated calcium alginate beads and the same beads were coated with three types of material, chitosan, sodium alginate, and poly-L-lysine in combination with alginate. The thickness of the alginate beads increased with the addition of coating materials. No differences were detectable in the bead strength by texture analysis or in the thickness of the beads with different types of coating materials by transmission electron microscopy. The survivability of three probiotics in uncoated beads, coated beads, and as free cells (unencapsulated) was conducted in 0.6% bile salt solution and simulated gastric juice (pH 1.55) followed by incubation in simulated intestinal juice with and without 0.6% bile salt. Chitosan-coated alginate beads provided the best protection for L. acidophilus and L. casei in all treatments. However, B. bifidum did not survive the acidic conditions of gastric juice even when encapsulated in coated heads. (C) 2004 Elsevier Ltd. All rights reserved.

Hydraulic properties of layered soils influence survival of Rhodes grass (Chloris gayana Kunth.) during water stress

Survival of vegetation on soil-capped mining wastes is often impaired during dry seasons due to the limited amount of water stored in the shallow soil capping. Growth and survival of Rhodes grass (Chloris gayana) during soil drying on various layered capping sequences constructed of combinations of topsoil, subsoil, seawater-neutralised residue sand and low grade bauxite was determined in a glasshouse. The aim was to describe the survival of Rhodes grass in terms of plant and soil water relationships. The soil water characteristic curve and soil texture analysis was a good predictor of plant survival. The combination of soil with a high water holding capacity and low soil water diffusivity (e.g. subsoil with high clay contents) with soil having a high water holding capacity and high diffusivity (e.g. residue sand) gave best survival during drying down (up to 88 days without water), whereas topsoil and low grade bauxite were unsuitable (plants died within 18-39 days). Clayey soil improved plant survival by triggering a water stress response during peak evaporative water demand once residue sand dried down and its diffusivity fell below a critical range. Thus, for revegetation in seasonally dry climates, soil capping should combine one soil with low diffusivity and one or more soils with high total water holding capacity and high diffusivity.

Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring vegetation structural parameters. The objective of this work was to evaluate Ikonos and Landsat-7 ETM+ imagery for mapping structural parameters and species composition of riparian vegetation in Australian tropical savannahs for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from Ikonos data were used for estimating leaf area index (R-2 = 0.13) and canopy percentage foliage cover (R-2 = 0.86). Pan-sharpened Ikonos data were used to map riparian species composition (overall accuracy = 55 percent) and riparian zone width (accuracy within +/- 3 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.