Comparing Three Spaceborne Optical Sensors via Fine Scale Pixel-based Urban Land Cover Classification Products

Accessibility to higher resolution earth observation satellites suggests an improvement in the potential for fine scale image classification. In this comparative study, imagery from three optical satellites (WorldView-2, Pleiades and RapidEye) were used to extract primary land cover classesfrom a pixel-based classification principle in a suburban area. Following a systematic working procedure, manual segmentation and vegetation indices were applied to generate smaller subsets to in turn develop sets of ISODATA unsupervised classification maps. With the focus on the land cover classification differences detected between the sensors at spectral level, the validation of accuracies and their relevance for fine scale classification in the built-up environment domain were examined. If an overview of an urban area is required, RapidEye will provide an above average (0.69 k) result with the built-up class sufficiently extracted. The higher resolution sensors such as WorldView-2 and Pleiades in comparison delivered finer scale accuracy at pixel and parcel level with high correlation and accuracy levels (0.65-0.71k) achieved from these two independent classifications

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

The accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result.Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results

Comparing three spaceborne optical sensors via fine scale pixel based urban land cover classification products

Accessibility to higher resolution earth observation satellites suggests an improvement in the potential for fine scale image classification. In this comparative study, imagery from three optical satellites (WorldView-2, Pléiades and RapidEye) were used to extract primary land cover classes from a pixel-based classification principle in a suburban area. Following a systematic working procedure, manual segmentation and vegetation indices were applied to generate smaller subsets to in turn develop sets of ISODATA unsupervised classification maps. With the focus on the land cover classification differences detected between the sensors at spectral level, the validation of accuracies and their relevance for fine scale classification in the built-up environment domain were examined. If an overview of an urban area is required, RapidEye will provide an above average (0.69 κ) result with the built-up class sufficiently extracted. The higher resolution sensors such as WorldView-2 and Pléiades in comparison delivered finer scale accuracy at pixel and parcel level with high correlation and accuracy levels (0.65-0.71 κ) achieved from these two independent classifications.http://www.sajg.org.zaam201

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

The accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result. Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results.http://www.sajg.org.za/index.php/sajgam2016Centre for Geoinformation ScienceGeography, Geoinformatics and Meteorolog