D-SS Frame:deep spectral-spatial feature extraction and fusion for classification of panchromatic and multispectral images

Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. The proposed approach combines spectral and spatial information based on the fusion of features extracted from panchromatic( PAN) and multispectral( MS) images using sparse autoencoder and its deep version. There are three steps in the proposed method,the first one is to extract spatial information of PAN image,and the second one is to describe spectral information of MS image. Finally,in the third step,the features obtained from PAN and MS images are concatenated directly as a simple fusion feature. The classification is performed using the support vector machine( SVM) and the experiments carried out on two datasets with very high spatial resolution. MS and PAN images from WorldView-2 satellite indicate that the classifier provides an efficient solution and demonstrate that the fusion of the features extracted by deep learning techniques from PAN and MS images performs better than that when these techniques are used separately. In addition,this framework shows that deep learning models can extract and fuse spatial and spectral information greatly,and have huge potential to achieve higher accuracy for classification of multispectral and panchromatic images.

Extraction of Aluminum and Iron from Boiler Slag by Sulfuric Acid

This paper presents a new method of recycling aluminum and iron in boiler slag derived from plants that use coal as fuel. The new method integrates efficient extraction and reuse of the leached pellets together. An elemental analysis of aqueous solutions leached by sulfuric acid was determined by EDTA-Naz-ZnCl2 titration method. The components and microstructures of the samples were examined by means of XRF, XRD and SEM. An aluminum extraction efficiency of 86.50% was achieved when the sintered pellets were leached using 4 mol · L^- 1 H2SO4 at solid/ liquid [m（g）/V（mL）] ratio of 1 ： 5 at 80 ℃ for 24 h. An iron extraction efficiency of 94.60% was achieved in the same conditions for the maximum extraction efficiency of Al. The extraction efficiencies of Al and Fe increased with an increase in temperature, leaching time and acidity. The concentration of alumina and iron hydroxide in the final product was determined to be 99.12% and 92.20% respectively. This product of alumina would be used directly for the production of metallic aluminum.

Extraction of Metals from Boiler Slag by Sintering

A new method of recycling aluminum and iron in boiler slag derived from plants that use coal as fuel was presented. The new method can integrate efficient extraction and reuse of the leached pellets together. An elemental analysis of aqueous solutions leached by sulfuric acid was conducted by the EDTA-Na2-ZnCl2 titration method, and the components and microstructures of the samples were examined by means of XRF, XRD and SEM. An aluminum extraction efficiency of 86.50% was achieved when the sintered pellets were leached using 4 mol·L-1 H2SO4 with solid/liquid ratio（m/V） of 1∶5 at 80 ℃ for 24 h. An iron extraction efficiency of 94.60% was achieved under the same condition for the maximum extraction efficiency of Al. The extraction efficiency of Al and Fe increased with temperature, leaching time and acidity. The concentration of alumina and iron hydroxide in the final product was determined to be 99.12% and 92.20% respectively. This product of alumina would be used directly for the production of metallic aluminum.