Photonic materials with angular independent structural colour are highly desirable because they offer the broad viewing angles required for application as colorants in paints, cosmetics, textiles or ...displays. However, they are challenging to fabricate as they require isotropic nanoscale architectures with only short-range correlation. In this article, porous microparticles with such a structure are produced in a single, scalable step from an amphiphilic bottlebrush block copolymer. This is achieved by exploiting a novel controlled micellization self-assembly mechanism within emulsified toluene-in-water droplets. By restricting water permeation through the droplet interface, the size of the pores can be precisely addressed, resulting in structurally coloured pigments. Furthermore, the reflected colour can be tuned to reflect across the full visible spectrum using only a single polymer (Mn = 290 kDa) by altering the initial emulsification conditions. Such photonic pigments have several key advantages over their crystalline analogues, as they provide isotropic structural coloration that suppresses iridescence and improves colour purity without the need for either refractive index matching or the inclusion of a broadband absorber.
To achieve the aim of classification for Polarimetrie synthetic aperture radar (PolSAR) images, the supervised classification approach based on sparse coding of covariance matrix is proposed in this ...paper. Being different from traditional classification methods which are based on polarization features extraction or statistical distribution models, our method research the sparse coding algorithm for covariance matrices under the circumstances of Riemannian manifold. The proposed method first obtains the coding dictionary by using k-means clustering. Then, each covariance matrix is decomposed into the sparse linear combination of those atoms in the coding dictionary via Riemannian sparse coding approach. Finally, the sparse coding coefficients are used as the feature vectors to obtain the final classification results with support vector machine (SVM) classifier. The experimental results of different real PolSAR images demonstrate that our method is effectiveness.
