A modified mean filter for improving the classification performance of very high-resolution remote-sensing imagery

Very high resolution (VHR) remote-sensing imagery can reveal ground objects in great detail, depicting the colour, shape, size and structure of the objects. However, VHR also leads to a large amount of noise in the spectra, which may reduce the reliability of the classification result. This article presents an extension of the mean filter (MF), which is named ‘modified mean filter (MMF)’, for smoothing the noise of VHR imagery. First, the MMF is a shapeadaptive filter that is constructed by gradually detecting the spectral similarity between a kernel-anchored pixel and its contextual pixels through an extension detector with eight neighbouring pixels. Then, because pixels of an objective are usually homogeneous with spatial continuity, the pixels located at the hollow area within an extended region are rectified to enhance the smoothing effect. Finally, the spectral value of the kernel-anchored pixel is determined by the mean of the group of pixels within the adaptive region. Despite the proposed filter is a simple extension of MF, it has an advantage in preserving the edge between different classes, and smoothing the noise of intra-class. The MMF approach is investigated through comparing with the classification of VHR images based on filter processing, including the traditional mean filter (MF), the median filter (MedF) and the recursive filter (RF) which has been proposed for image classification in Kang, Li, and Benediktsson (2014). The experimental results obtained by considering two VHR images show the effectiveness of the proposed of MMF, which improves the performance of the classification and implies more potential applications.