Earth observation via optical-based remote sensing is one of the effective solutions to cover the large swath and to deliver the very high-resolution dataset at the different wavelengths. But the applicability of optical imaging is limited by daytime only and adversely affected by the presence of clouds. In such scenarios, microwave data is more preferable due to the potential of penetrating through the clouds. Recently launched (26 September 2016) scatterometer satellite (SCATSAT-1) data by the Indian Space Research Organization (ISRO) has the potential of providing all-weather, day-night monitoring and daily data-delivery services at the global level. Along with the numerous advantages, the Ku-band (13.535 GHz) based SCATSAT-1 cannot provide sufficient information as provided by the multispectral optical sensors. Therefore, in the present work, the microwave-based SCATSAT-1 and optical-based MODIS (moderate resolution imaging spectroradiometer) have been fused using the nearest-neighbour approach to examine its effects in cloud removal and its applications in classification. The study has been performed over Himachal Pradesh, India. This study has also discussed the impact of different classifiers such as artificial neural network (ANN), spectral angle mapper (SAM), support vector machine (SVM), and random forest (RF), on the fusion of SCATSAT-1 (including backscattered coefficients, i.e. sigma-nought and gamma-nought at HH and VV polarizations) and MODIS dataset. Experimental results have confirmed that the accuracy of implemented classified maps significantly increases with the fusion of both datasets as compared to the individual implementation of SCATSAT-1- and MODIS-classified maps. From quantitative analysis, the RF classifier performs better as compared to other classifiers, i.e. ANN, SAM, and SVM on the fused dataset. This study has many applications in the near real-time monitoring of snow/ice, agriculture activities, and hydrological studies.

通过基于光学的遥感进行地球观测是覆盖大片区域并提供不同波长的超高分辨率数据集的有效解决方案之一。但是光学成像的适用性仅受白天的限制，并且受到云的存在的不利影响。在这种情况下，微波数据更可取，因为它具有穿透云层的潜力。印度空间研究组织（ISRO）最近发射的（2016 年 9 月 26 日）散射计卫星（SCATSAT-1）数据具有在全球范围内提供全天候、昼夜监测和日常数据传输服务的潜力。除了众多优势之外，基于 Ku 波段 (13.535 GHz) 的 SCATSAT-1 无法提供多光谱光学传感器提供的足够信息。因此，在目前的工作中，基于微波的 SCATSAT-1 和基于光学的 MODIS（中分辨率成像光谱仪）已使用最近邻方法融合，以检查其在云去除方面的影响及其在分类中的应用。该研究是在印度喜马偕尔邦进行的。本研究还讨论了人工神经网络 (ANN)、光谱角度映射器 (SAM)、支持向量机 (SVM) 和随机森林 (RF) 等不同分类器对 SCATSAT-1（包括反向散射）融合的影响。系数，即 HH 和 VV 极化下的 sigma-nought 和 gamma-nought）和 MODIS 数据集。实验结果证实，与单独实施 SCATSAT-1 和 MODIS 分类地图相比，实施分类地图的准确性随着两个数据集的融合而显着提高。从定量分析来看，RF 分类器在融合数据集上的性能优于其他分类器，即 ANN、SAM 和 SVM。这项研究在雪/冰的近实时监测、农业活动和水文研究中有许多应用。