Abstract
Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. <br><br> In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. <br><br> To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. <br><br> The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.
Highlights
Large datasets of aerial imagery exist in different government institutions, such as national land, urban planning, and construction departments (Lv et al, 2010)
Column 1 specifies the composition of the classification vector employed, column 2 shows the metric used, and the following twelve columns list the results of the various experiments
The rows are ordered considering the global average agreement obtained in the various tests, and secondly their median value. Following this criterion a significant outcome emerges: classifications exploiting invariant components are generally superior than those based on red-green-blue colour components (RGB) and texture
Summary
Large datasets of aerial imagery exist in different government institutions, such as national land, urban planning, and construction departments (Lv et al, 2010). The urban environment in particular represents one of the most challenging problems for remote sensing analysis as a consequence of high spatial difference and spectral variance of the surface materials (Herold et al, 2003) This question can be overcome with different techniques that take into account two fundamental image characteristics: colour (spectral information) and texture (Haralick et al, 1973). Lv et al (2010) developed a method to carry out land cover classifications that depend only on RGB bands reaching high values of accuracy They observed that in order to extract building information, it is necessary to include spectral and texture features. The tests carried out aim to evaluate the actual improvements in high resolution colour image classification provided by textural information and spatial resolution resampling
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