Modelling of air temperature using remote sensing and artificial neural network in Turkey

The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.     

Automatic extraction of lineaments based on wavelet edge detection and aided tracking by hillshade

Lineaments refer to the linear or curvilinear textures on remote sensing image, whose general spatial distribution characteristics are often the response of deep geological sturcture at the surface. Firstly, we use wavelet modulus maximum transformation to detect the edges with 4 scales on Landsat – 8 OLI B5 image and analyze their multi-scale characteristics. As the result, it is determined that the optimal scale of edge detection is 4, and the outline that consist of the edge pixels is roughly corresponding to the geological structure of mine area. Thus the incomplete lineaments have been extracted by using the 2D otsu algorithm. Secondly, the hillshade map generated based on DEM is processed to generate binarized linear shadow. Finally, the linear shadow is superimposed on the lineaments preliminarily extracted to obtain the optimized lineaments. Experiment results show that, based on the method, there are some deformation and displacement between the lineaments extracted and the actual geological structure, and it fail to effectively extract Qilinchang Fault, but lineaments are in good correspondence with Kuangshanchang Fault, Dongtou Fault and Niulan River Fault, which are basically in accord with the geological structure framework of the mine area.     

Scheduling algorithms for rapid imaging using agile Cubesat constellations

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ～40?kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20?kg satellites in Low Earth Orbit for two case studies – rapid imaging of Landsat’s land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that optimality of the dynamic programming solution for single satellites was within 10%, and find up to 5% more optimal solutions. The optimality gap for constellations was found to be 22% at worst, but the dynamic programming schedules were found at nearly four orders of magnitude better computational speed than integer programming. The algorithm can include cloud cover predictions, ground downlink windows or any other spatial, temporal or angular constraints into the orbital module and be integrated into planning tools for agile constellations.     

Lineament mapping and fractal analysis using SPOT-ASTER satellite imagery for evaluating the severity of slope weathering process

The present study describes a remote sensing approach for preparing lineament map that subsequently indicates the influence of lineament density in the severity of weathering development. In this study, SPOT-5 data, the integration of SPOT-ASTER and Digital Elevation Model (DEM) data were used and processed. The existence of an active fault system in the south of Mashhad city, NE Iran and presence of schistose rocks in this area result in the development of numerous lineament features. This region was selected for this research. Lineament features including fractures, bedding plane, cleavage, shear zones and schistosity were mapped in the study area. The results indicate that the highest concentration of lineaments occurred in the central-western and south-eastern parts of the study area, which coincide with metamorphic outcrops and NW-SE trending fault system. A comparison of lineament statistical analysis and field survey demonstrated that the structural discontinuities have a significant effect on forming and distribution of weathering profiles. It was observed that increasing the number, length and density of structural discontinuities led to strong severity in weathering, which can produce deep residual soils susceptible to landslide occurrence. The remote sensing approach developed in this study can be applicable for preparing lineament maps and evaluating the severity of weathering development in other active fault zones around the world.     

真空低温环境红外亮温标准装置设计

对遥感技术现状及面临的问题进行了介绍，明确了建立真空低温环境红外亮温计量标准装置的意义，对国内外研究现状进行了梳理，对研究的标准装置的技术路线和设计方案进行了详细叙述。     

多波段光谱辐亮度计量值溯源技术研究

光谱辐亮度计是实现空间遥感仪器在地面实验室内进行高精度光谱定标的关键设备之一，本文基于标准探测器的辐亮度传递原理，利用视场光阑、孔径光阑、窄带滤光片和光电二极管研制了10测量通道的遮光筒式多波段光谱辐亮度计，测量波长分布在（400~1 100）nm范围。具有校准通道，与积分球单色光源匹配使用，实现光谱辐亮度的量值溯源，测量不确定度优于2.2%。     

空间主动光电遥感现状及发展

空间主动光电遥感技术在空间对地观测和深空探测等领域具有广阔的应用前景,探测对象包括地球和地外天体三维地形、地球植被垂直分布、大气气溶胶与云的垂直分布、大气温湿廓线、大气风廓线和特殊气体浓度等。为系统梳理空间主动光电遥感技术,综合分析了国内外空间主动光电高程探测载荷和大气垂直廓线探测载荷的发展历史和现状,提出了未来我国在多波束单光子三维地形探测、高光谱分辨率激光大气探测、差分吸收激光大气探测、激光掩星大气探测和激光海洋探测领域开展更深入研究的建议。     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.