针对传统的舰船检测算法无法有效避免旁瓣效应对结果的影响,及多考虑舰船与背景之间的灰度对比度而未充分利用SAR影像上目标对象的几何特征造成检测精度较低的问题,提出了一种基于舰船多特征的目标检测算法.该方法利用方位角估算法与逐步逼近法剔除旁瓣效应对计算目标对象几何特征(面积、长宽比和矩形度)及灰度对比度特征的影响,利用变异系数法赋予4个特征不同的权重,计算出目标对象的置信度,选取最佳置信阈值,剔除非目标对象,优化检测结果.利用Sentinel-1影像数据对算法进行了验证,并将其与双参数CFAR算法和KSW双阈值算法进行了对比实验.实验结果表明:对于3张背景复杂度不同的影像,所提出的算法质量因子均超过了0.7且耗时最短,同时对于背景较为复杂的影像仍能保持较好的检测性能.

Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields.

Excessive urban growth has led to an urban environmental degradation in megacities in less developed countries. Using fine particulate matter (PM2.5) concentration, land surface temperature (LST), and normalized difference vegetation index (NDVI) data obtained by satellite remote sensing, we analysed the inter-annual variations and trends in the urban environment of 17 megacities in Eurasia from 2000 to 2016. Taking the average environmental condition for all the megacities in 2000 as the baseline, the urban environmental conditions were evaluated by a Comprehensive Environmental Index (CEI) from 2001 to 2016. The variation and trends analysis of CEI revealed that the overall environmental conditions in Chennai, Dhaka, Kolkata and Tianjin showed significant deterioration trends. Environmental qualities in newly developed urban areas experienced degradation in Bangalore, Beijing, and Mumbai. The area of environmentally deteriorated urban land has been expanding in Bangalore, Chennai, Delhi, Kolkata, and Mumbai in India and Dhaka in Bangladesh since 2001. By contrast, the area of environmentally degraded urban land in Chinese megacities expanded to the largest extent in the period of 2007-2009 and decreased afterwards. The result suggests that greening and strong emission control strategies significantly contributed to urban environmental quality enhancement in rapidly developing megacities.

As the World Health Organization (WHO) has reported, air pollution both indoor and outdoor caused nearly 7 million deaths in 2012. Due to the rapid process urbanization and industrialization, air pollution in the form of fine particle matter continues to be a severe environmental problem in large cities in China. In particular, PM2.5 refers to aerosol particles smaller than 2.5 μm in diameter that are suspended in the air. According to previous studies, human exposure to PM2.5 can cause development of various respiratory and cardiopulmonary diseases. Thus, quantifying human exposure to PM2.5 is necessary for public health risk assessment. Satellite remote sensing has proved to be a cost-effective tool for PM2.5 concentration estimation. In our study, the ground-level fine particulate matter (PM2.5) concentration dataset with a spatial resolution of 1 km was used to analyze spatial temporal variations of PM2.5 concentration from 2000 to 2014 along the Maritime Silk Road. The inter-annual changing trends of PM2.5 in 12 large urban agglomerations along the Maritime Silk Road were investigated in detail. The urban agglomerations including Dhaka, Kolkata and Mumbai have experienced the most rapid increase of PM2.5 concentration with the average annual rates of 1.08 ug/m3, 1.44 ug/m3 and 0.79 ug/m3, respectively from 2000 to 2014. The areal extent of PM2.5 concentration over 35 ug/m3 increased from 15.2% in 2000 to 64.5% in 2014 in the large urban agglomerations. These findings can provide useful information for reducing human risk to air pollution and promote sustainable urban development in China and countries along the Maritime Silk Road.

随着城市化进程的加快,如何及时、精确地对城市环境的变化做出评价,进而制定出合理的发展方案,对城市可持续发展至关重要.本文综合利用卫星遥感获取的PM2.5浓度数据、地表温度数据(Land Surface Temperature,LST)、植被指数数据(Normalized Difference Vegetation Index,NDVI)及城市用地辅助信息数据,基于综合评价指标,分析海上丝绸之路沿线12个超大城市地区2000-2013年环境质量的动态变化.研究结果表明,2000-2013年,海上丝绸之路沿线约75％的超大城市呈现出不同程度的环境恶化现象.12个超大城市用地环境恶化及逐步恶化面积占研究区域总面积的31.33％(4732.39 km2).2000-2013年,城市扩张用地恶化和逐步恶化面积约占总扩张用地的29.48％(3765.83 km2).平均地表温度的上升、植被覆盖度的急剧下降及PM2.5浓度的增加均对海上丝绸之路沿线超大城市环境质量变化产生影响.其中,空气中PM2.5浓度的大幅度增加是2000-2013年海上丝绸之路沿线超大城市扩张用地环境退化的主要原因.