COSMO-SkyMed时序影像南京城市变化检测研究

南京城市地表要素更新快且天气多云雨，光学影像无法提供实时数据；而合成孔径雷达（SAR）因其全天时、全天候工作特性，可有效弥补常规遥感数据获取瓶颈问题。以南京河西新城和江北新区为示范，选取2016~2018年13景COSMO-SkyMed数据，采用相干系数差值和强度RC合成法进行城市变化检测。针对SAR斑点噪声，分别采用同质滤波及非局部滤波法对干涉图和强度图进行降噪。两种变化检测方法的数据处理与性能评估结果表明：降噪滤波技术提升SAR城市变化图斑信息提取能力，两种方法产出查准率均为94%左右；然而目前方法仍可存在漏检，对应概率分别为66.8%和29.6 %。相较于前者，基于强度RC合成法具备对小面元、线型地物变换更佳提取能力；同时考虑到该方法对时空基线无要求，因此在实际多云多雨城市变化检测中具有更好的应用价值与推广潜力。

Urban Change Detection based on COSMO-SkyMed Multi-temporal Images in Nanjing City,China

Influenced by the rainy and cloudy monsoonal climate, optical remote sensing has limitations in the mapping of land use and land cover change of Nanjing City under rapid urbanization. Alternatively, Synthetic Aperture Radar (SAR) provides a feasible solution owing to the operation capacity in all-time and all-weather conditions. In this study, taking Hexi New Town and Jiangbei developing District (Nanjing) as example, we jointly applied coherent and intensity RC composition for the SAR change detection using thirteen COSMO-SkyMed images acquired in the period from 2016 to 2018. To reduce impacts from the speckle, we respectively applied Statistically Homogeneous Pixel Selection (SHPS) and Non-Local (NL) filters to coherence and intensity SAR images. The performance comparison of two aforementioned change detection approaches indicate that both of them achieved a reliable correct detection probability (up to 94%), in particular when the adaptive filters were employed. However, the difference of the omission probability from them were evident, resulting in 66.8% of the coherent compared to 29.6 % of the intensity RC composition. Generally, the intensity RC composition is more sensitive to the change of small patches and linear features. In addition, this method is not constrained by data processing and acquisitions, such as the requirements of spatiotemporal baselines. In summary, the intensity RC composition has a better performance and potential in the urban change detection, in particular for regions where rainy and cloudy climate is prevailing.