Content adaptive video denoising based on human visual perception

In this paper, we propose content adaptive denoising in highly corrupted videos based on human visual perception. We introduce the human visual perception in video denoising to achieve good performance. In general, smooth regions corrupted by noise are much more annoying to human observers than complex regions. Moreover, human eyes are more interested in complex regions with image details and more sensitive to luminance than chrominance. Based on the human visual perception, we perform perceptual video denoising to effectively preserve image details and remove annoying noise. To successfully remove noise and recover the image details, we extend nonlocal mean filtering to the spatiotemporal domain. With the guidance of content adaptive segmentation and motion detection, we conduct content adaptive filtering in the YUV color space to consider context in images and obtain perceptually pleasant results. Extensive experiments on various video sequences demonstrate that the proposed method reconstructs natural-looking results even in highly corrupted images and achieves good performance in terms of both visual quality and quantitative measures.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

基于高时空分辨率卫星数据的气溶胶光学厚度反演系统

卫星遥感技术是研究大气气溶胶时空变化规律的重要手段之一。高分四号卫星(GF-4)作为新一代静止卫星,不仅具备更多的可见-近红外波段,而且具有超高时空分辨率的特点,可以执行对固定区域连续观测或凝视扫描覆盖观测,为气溶胶光学厚度定量反演提供了有利条件。基于GF-4静止卫星超高时空分辨率卫星数据,在深入了解卫星观测模式及传感器波段对气溶胶反演敏感性基础上,基于地气时变差异核心思路,开发了针对GF-4超高时空分辨率数据气溶胶光学厚度反演算法;设计并实现了具备业务化反演气溶胶光学厚度能力的软件系统,软件系统具备多线程、全自动运行能力,满足卫星数据处理业务化需求。利用该系统对GF-4数据进行了处理和分析,通过地基仪器实测的气溶胶光学厚度进行了验证,取得了初步结果。结果表明,该系统具有良好的可靠性和稳定性,可以服务于大气颗粒物时空变化遥感监测。     

南方丘陵区植被覆盖度遥感估算与时空变化研究——以福建省永定县为例

植被覆盖度是生态环境监测的重要指标,而复杂地形因素影响对山地植被遥感信息准确提取。基于Landsat-8OLI遥感数据,分别采用像元二分模型和线性混合光谱分解法,在对比分析植被覆盖度的地形敏感性基础上,选择山地植被指数(NDMVI)估算了1992、2002和2014年永定县的植被覆盖度,并分析其变化。结果表明:1基于山地植被指数(NDMVI)的覆盖度估算模型的地形敏感性最弱,更适合于南方丘陵山地的植被覆盖度遥感反演;2永定县总体植被覆盖度较高,平均植被覆盖度达77.99%以上,高覆盖度区占59.73%以上,22年内植被覆盖度经历了先提高再下降的过程;3在空间上,高坎抚、金丰和西部片区的植被覆盖度较低,动态变化较明显。永定县金丰片区植被覆盖度明显提高;而近12年内高坎抚片区因矿业开采活动对生态环境的破坏,植被覆盖度降低幅度大,且变化面积较大。     

Conceptual design of a Moon-based Earth radiation observatory

An Earth radiation observatory on the nearside of the Moon could provide long-term measurements complementary to the satellite-based Earth radiation observation data. This study is intended as a preliminary step towards the design of a Moon-based Earth Radiation Observatory (MERO) and its instrumentation. Based on the analysis of data from the JPL Horizons Ephemeris System for an 18.6-years precession cycle, we show that due to the irregularities of the lunar orbit, for a MERO to maintain an all-time whole Earth disk coverage, it needs to be located within the region of 80.5º W–80.5º E and 81.5º S–81.5º N on the nearside of the Moon and requires a minimum angular instantaneous field of view of 2.07º. Additionally, it should have a 360º horizontally and 15.5º vertically orientation adjustability. On one hand there is a risk for a MERO instrument to receive direct sunlight which might excess the radiation allowance of the sensor; on the other hand, a MERO would have a better polar coverage than does a low orbit spacecraft borne Earth observatory. Within the deployable region, a multi-station MERO network may increase the instantaneous spatial coverage by up to around 10% with abundant overlaps to facilitate inter calibration and validation.     

GIS-model based estimation of nitrogen leaching from croplands of China

Nitrogen (N) is the most widely used fertilizer nutrient, and its application has increased substantially in recent decades in China. N loss through leaching has been recognized as one of the most common agricultural sources of groundwater contamination. Thus, prediction of N leaching from cropland is crucial for preventing groundwater pollution. This paper quantifies nitrogen leaching from China’s croplands, identifies its spatial distribution under current cropping systems at national scale, and finally puts forward some policies or strategies to reduce rates of N leaching. A computer process simulation model of carbon and nitrogen biogeochemistry in agro-ecosystems (DNDC) was applied to predict nitrogen leaching in the soil layer of agricultural ecosystems at national scale. Data on climate, soil properties, cropping systems, acreage, and management practices at county scale were collected from various sources and integrated into a spatial GIS database to run the model. The total amount of N-leaching was predicted at 4.57 million t N/year, which is equivalent to 48 kg N per ha cropland in 1998. The spatial distribution of N leaching in China showed a sharp discrepancy between the northern and southern counties due to the differences in climatic conditions, soil properties, as well as farm management practices. The study also suggests that applying management alternatives, such as proper fertilizer, crop, water and soil management, could be efficient means for decreasing N leaching rates.     

基于全极化雷达数据反演夏玉米覆盖农田表层土壤含水量

基于RADARSAT-2全极化C波段雷达数据和野外实测数据,建立了不同极化方式的后向散射系数与夏玉米植被含水量的响应关系,获得了山东禹城夏玉米覆盖区域农作物含水量。在此基础上,对＂水—云模型＂的参数进行了修改,从总的极化雷达后向散射中去除了植被影响,获取了土壤组分的后向散射系数,从而能够通过雷达多极化数据得到表层土壤含水量。结果表明：夏玉米植被含水量观测值与VH、VV后向散射系数的相关性均达到显著水平,相关系数分别为0.86和0.79,VH极化优于VV极化;在RADARSAT-2四种极化方式下,VV极化下的＂水—云模型＂理论计算的误差平方和最小,是研究区表层土壤含水量反演的最优极化方式。