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611.
Huanfeng Shen Yaolin Liu Tinghua Ai Yi Wang Bo Wu 《International Journal of Applied Earth Observation and Geoinformation》2010
The performance of remote sensing images in some applications is often affected by the existence of noise, blurring, stripes and corrupted pixels, as well as the hardware limits of the sensor with respect to spatial resolution. This paper presents a universal reconstruction method that can be used to improve the image quality by performing image denoising, deconvolution, destriping, inpainting, interpolation and super-resolution reconstruction. The proposed method consists of two parts: a universal image observation model and a universal image reconstruction model. In the observation model, most degradation processes in remote sensing imaging are considered in order to relate the desired image to the observed images. For the reconstruction model, we use the maximum a posteriori (MAP) framework to set up the minimization energy equation. The likelihood probability density function (PDF) is constructed based on the image observation model, and a robust Huber–Markov model is employed as the prior PDF. Experimental results are presented to illustrate the effectiveness of the proposed method. 相似文献
612.
《Quaternary Science Reviews》1995,14(1):1-16
Direct evidence for Late Weichselian grounded glacier ice over extensive areas of the Barents Sea is based largely on indirect observations, including elevations of old shorelines on Svalbard and arguments of isostatic rebound. Such isostatic models are discussed here for two cases representing maximum and minimum ice-sheet reconstructions. In the former model the ice extends over the Kara Sea, whereas in the latter the ice is limited to the Barents Sea and island archipelagos. Comparisons of predictions with observations from a number of areas, including Spitsbergen, Nordaustlandet, Edgeøya, Kong Karls Land, Franz Josef Land, Novaya Zemlya and Finnmark, support arguments for the existence of a large ice sheet over the region at the time of the last glacial maximum. This ice sheet is likely to have had the following characteristics, conclusions that are independent of assumptions made about the Earth's rheological parameters. (i) The maximum thickness of this ice was about 1500–2000 m with the centre of the load occurring to the south and east of Kong Karls Land. (ii) The ice sheet extended out to the western edge of the continental shelf and its maximum thickness over western Spitsbergen was about 800 m. (iii) To the north of Svalberg and Frans Josef Land the ice sheet extended out to the northern shelf edge. (iv) Retreat of the grounded ice across the southern Barents Sea occurred relatively early such that this region was largely ice free by about 15,000 BP. (v) By 12,000 BP the grounded ice had retreated to the northern archipelagos and was largely gone by 10,000 BP. (vi) The ice sheet may have extended to the Kara Sea but ice thicknesses were only a fraction of those proposed in those reconstructions where the maximum ice thickness is centered on Novaya Zemlya. Models for the palaeobathymetry for the Barents Sea at the time of the last glacial maximum indicate that large parts of the Barents Sea were either very shallow or above sea level, providing the opportunity for ice growth on the emerged plateaux, as well as on the islands, but only towards the end of the period of Fennoscandian ice sheet build-up. 相似文献
613.
地面核磁共振(SNMR)找水方法是目前唯一能直接找到地下水的地球物理新方法。由于该方法接收灵敏度高,因此对电磁噪声也很敏感。为了提高信噪比和新方法的找水效果,必须对接收到的信号进行去噪处理。本文使用了小波分析去噪提高核磁共振信号信噪比的方法,并通过对湖北省新洲工区和王家湾工区实测数据的小波处理和对比分析,证明了这种方法的有效性。 相似文献
614.
615.
为进一步研究岩石破裂电磁辐射特征,本文根据岩石破裂电磁辐射是由岩石破裂时产生带电粒子扰动引起的假说,通过断裂力学理论中的张开位移法计算岩石破裂过程中的裂纹宽度,由电磁辐射频率与破裂宽度之间的关系,研究电磁辐射频率与弹性参数之间的关系,并给出了它们之间的关系表达式.通过建立具有中心贯穿裂纹的无限大平板模型,根据破裂宽度计算了在该模型条件下几种岩石破裂过程中产生电磁辐射频率范围.理论模型计算出来的频率范围主要集中在实验观测的中高频段,并体现出频率随弹性参数的变化. 相似文献
616.
617.
《地学前缘(英文版)》2022,13(5):101425
Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation. 相似文献