A Mean Shift Vector-Based Shape Feature for Classification of High Spatial Resolution Remotely Sensed Imagery

Abstract

The development of very high spatial resolution remote sensing sensors opens a new era for mapping the earth with submeter level of detail, whereas the increased resolution brings about difficulties for the land-cover classification in terms of intra-class variability and inter-class similarity. This paper presents a novel spatial feature, mean shift (MS) vector-based shape feature (MSVSF), to improve the classification accuracy of very high resolution (VHR) remote sensing imagery. MSVSF is a ${\bf 3} \times {\bf 1}$ feature vector extracted in per-pixel fashion. It describes the shape of a spectrally homogeneous area surrounding each pixel by measuring the two-dimensional (2-D) image deformation of its local area imposed by the MS vector. The proposed feature is particularly effective to discriminate objects with similar spectral response but different 2-D shapes, such as buildings and roads. Independent component analysis is adopted to extract spectral features and Support Vector Machine (SVM) classifier is adopted to classify the spectral and spatial features and several state-of-the-art spatial/structural features are compared to the proposed feature. A synthetic experiment demonstrates that the proposed feature has good capability to describe 2-D shapes with different scale, two real dataset experiments on QuickBird and IKONOS images show MSVSF has achieved better overall accuracy (OA) than the compared ones. In addition, the MSVSF feature is extended to the object-based classification (OBC), and the result shows that the MSVSF is effective to improve the classification accuracy on high resolution images of the urban area.