PW-COG: An Effective Texture Descriptor for VHR Satellite Imagery Using a Pointwise Approach on Covariance Matrix of Oriented Gradients
Minh‐Tan Pham, Grégoire Mercier, Julien Michel · IEEE Transactions on Geoscience and Remote Sensing · 2016
In this paper, a novel algorithm for textural feature description in very high resolution (VHR) satellite imagery is developed. It is based on a pointwise (PW) approach on the feature covariance matrix. The main motivation of this work is to construct the covariance matrix of oriented gradients (COG) using a nondense approach based on characteristic points (i.e., keypoints) extracted from the image. The proposed descriptor, which is named PW-COG, is expected to be effective when applied to VHR images. First, a COG-based descriptor is capable of not only capturing both radiometric and local geometric information (given by gradient features) from the image but also encoding their joint distribution and correlation, which are effectively relevant for texture characterization and discrimination. Second, by employing a keypoint-based approach, the proposed method is able to deal with large amount of VHR image data, since we do not take into consideration all pixels of the image, without requiring the stationarity hypothesis. In order to demonstrate the effectiveness of the proposed descriptor, texture-based image classification is carried out. Experimental study on both Brodatz texture database and VHR satellite images using the proposed algorithm provides very competitive results, in terms of texture discrimination and algorithm complexity, compared to reference methods.