Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification

International audienceThis article introduces an extension of morphological attribute profiles (APs) by extracting their local features. The so-called local feature-based attribute profiles (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e. APs' sample) within its neighborhood, hence better deal with local texture information from the image content. In this work, LFAPs are constructed by extracting some simple first-order statistical features of the local patch around each APs' sample such as mean, standard deviation, range, etc. Then, the final feature vector characterizing each image pixel is formed by combining all local features extracted from APs of that pixel. In addition, since the self-dual attribute profiles (SDAPs) has been proved to outperform the APs in recent years, a similar process will be applied to form the local feature-based SDAPs (LFSDAPs). In order to evaluate the effectiveness of LFAPs and LFSDAPs, supervised classification using both the Random Forest and the Support Vector Machine classifiers is performed on the very high resolution Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show that LFAPs (resp. LFSDAPs) can considerably improve the classification accuracy of the standard APs (resp. SDAPs) and the recently proposed histogram-based APs (HAPs)

Covariance-based texture description from weighted coherency matrix and gradient tensors for polarimetric SAR image classification

International audienceThe present paper proposes a texture-based unsupervised classification algorithm for fully polarimetric SAR (PolSAR) images. Here, the main motivation is to combine polarimetric information and local structure gradients from PolSAR image data to describe textural features and then use them for classification purpose. In this work, the notion of PolSAR image textures is characterized by two key features. First, the polarimetric coherency matrix is estimated using a weighted averaging operator based on patch similarity. Second, the image local geometry is taken into account by exploiting the structure gradient tensors. These characteristics are then integrated into texture descriptors via the approach of covariance matrix. Unsupervised classification stage is finally achieved by employing an adapted distance measure for covariance-based descriptors. Experiments performed on very high resolution complex PolSAR images using the proposed algorithm provide very promising results in terms of terrain classification and discrimination