DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

Preparation of Gradient Materials with Molten Salts Electrodeposition

A new way of preparing W–Cu functional gradient materials (FGM) with molten salts electrodeposition is studied. The results show that, with the conditions of current density 70 mA·cm−2, electrodeposition temperature 700 °C and bidirectional pulse electrodepositing for 30 minutes (min), the Cu–Ni gradient layer prepared under this condition is widely used dense and smooth. Fundamental to the preparation of Cu–Ni functional gradient layer, Cu–Ni is used as a cathode to deposit W. Under the current density of 50 mA·cm−2, the time of 20 min, with bidirectional pulse electrodeposition, the Cu+Ni+W gradient layer has uniform composition gradient change and larger thickness. The W–Cu gradient materials prepared in this study have good cohesiveness. The addition of Ni would promote the inter-diffusion of Cu and W, and increase the diffusion coefficient significantly.

Effect of Gradient Layer of TiO2- HA on Properties of the Ha Coated Ti-6Al-4V

The use of hydroxyapatite (HA) coating on metallic implants has always been a challenge due to a discrepancy between ceramic coating and metallic substrate properties. In this study, functionally graded TiO2-HA coating (FGC TiO2-HA), with bottom-up composition: 100% TiO2, 50% TiO2 -50% HA, 100% HA, produced by sol-gel method on the Ti-6Al-4V alloy substrate. The XRD, FTIR and DSC results signified desired phase achievement. The quality assessment and scratch resistance of FGC TiO2-HA in comparison with control samples (single-layer HA and double-layer TiO2-HA coatings) were assessed by SEM and FESEM images and nanoscratch test. According to the tests outcomes, The TiO2 intermediate layer increases the HA coating’s strength (22%) and surface hardness, while gradient layer of TiO2-HA improves (14%) this consequence by progressing the coating integrity.