Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach

Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segmentation. In the classification context, the extracted features are commonly concatenated into a long vector (also called stacked vector), on which is applied a conventional vector-based machine learning technique (e.g. SVM with Gaussian kernel). In this paper, we rather propose to use a sequence structured kernel: the spectrum kernel. We show that the conventional stacked vector-based kernel is actually a special case of this kernel. Experiments conducted on various publicly available hyperspectral datasets illustrate the improvement of the proposed kernel w.r.t. conventional ones using the same hierarchical spatial features.Comment: 8th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016), UCLA in Los Angeles, California, U.

Combining multiple resolutions into hierarchical representations for kernel-based image classification

Geographic object-based image analysis (GEOBIA) framework has gained increasing interest recently. Following this popular paradigm, we propose a novel multiscale classification approach operating on a hierarchical image representation built from two images at different resolutions. They capture the same scene with different sensors and are naturally fused together through the hierarchical representation, where coarser levels are built from a Low Spatial Resolution (LSR) or Medium Spatial Resolution (MSR) image while finer levels are generated from a High Spatial Resolution (HSR) or Very High Spatial Resolution (VHSR) image. Such a representation allows one to benefit from the context information thanks to the coarser levels, and subregions spatial arrangement information thanks to the finer levels. Two dedicated structured kernels are then used to perform machine learning directly on the constructed hierarchical representation. This strategy overcomes the limits of conventional GEOBIA classification procedures that can handle only one or very few pre-selected scales. Experiments run on an urban classification task show that the proposed approach can highly improve the classification accuracy w.r.t. conventional approaches working on a single scale.Comment: International Conference on Geographic Object-Based Image Analysis (GEOBIA 2016), University of Twente in Enschede, The Netherland

Synthesis of a Novel Ce-bpdc for the Effective Removal of Fluoride from Aqueous Solution

Ce-1,1′-biphenyl-4,4′-dicarboxylic acid (Ce-bpdc), a novel type of metal organic framework, was synthesized and applied to remove excessive fluoride from water. The structure and morphology of Ce-bpdc were measured by X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy. The effects, such as saturated adsorption capacity, HCO3-, and pH, were investigated. The optimal pH value for fluoride adsorption was the range from 5 to 6. The coexisting bicarbonate anions have a little influence on fluoride removal. The fluoride adsorption over the Ce-bpdc adsorbent could reach its equilibrium in about 20 min. The Ce-bpdc coordination complex exhibited high binding capacity for fluoride ions. The maximum adsorption capacity calculated from Langmuir model was high up to 45.5 mg/g at 298 K (pH = 7.0) and the removal efficiency was greater than 80%. In order to investigate the mechanism of fluoride removal, various adsorption isotherms such as Langmuir and Freundlich were fitted. The experimental data revealed that the Langmuir isotherm gave a more satisfactory fit for fluoride removal. Finally, the tested results of ground water samples from three places, Yuefang, Jiangji, and Sanyi which exhibited high removal efficiency, also demonstrate the potential utility of the Ce-bpdc as an effective adsorbent