Research of RGB bands Quick Bird image land cover classification of a sub-watershed in Kunming Dianchi Lake Basin

Lv, Peng; Wu, Guiying; Yi, Yan; Jian-ming, Chen; Liu, Yue; Zhou, Guoqiong

doi:10.1109/cisp.2010.5646941

Cited by 2 publications

(2 citation statements)

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“…Large datasets of aerial imagery exist in different government institutions, such as national land, urban planning, and construction departments (Lv et al, 2010). Commonly, the spatial resolution can be high or even very high, but the spectral resolution is often limited to RGB, and there is a general lack of the near-infrared bands (Laliberte and Rango, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Among these, Chust et al, (2008) used an initial RGB supervised classification as reference, and compared it with those automatically obtained adding further information -NIR, DTM, aspect, slopereaching a mean accuracy of 73.2% and 75.1% on the two test areas considered. 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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Land Use Classification From VHR Aerial Images Using Invariant Colour Components and Texture

Movia¹,

Beinat²,

Sandri³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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. 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. 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. 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%