The earth observation using remote sensing images is an inquisitive way to explore and evaluate the geo-resources of any specific area on the globe. In this regard, Indian Resourcesat-2A (R2A) remote sensing satellite plays an important role in monitoring the critical resources of our planet using its unique three tier imaging mechanism. Optical sensors on-board R2A have good spatial temporal resolution for diverse space borne applications. Most of these applications requires Surface Reflectance (SR) data product by removing the effects of intermittent atmospheric scattering and absorption. Radiative Transfer Models (RTM) are used to perform atmospheric correction which are computationally intensive, thus a look-up table (LUT) is utilized to interpolate intermediate values as a trade-off between accuracy and speed. However, the process of interpolation too becomes very compute intensive when a large enough LUT is used. The paper provides an approach to remove this trade-off by using multi-layered deep network to model interpolation as a regression problem. The proposed method generates highly accurate Deep SR product with a significant reduction in turn-around time. The experimental result shows that a speedup of 5x is achieved with the developed framework as compared to conventional interpolation-based approach for generation of R2A LISS-3 Deep SR scene data product. The Deep SR product is compared with pure 6SV generated product and R2 value found to be 0.97 (Green), 0.97 (Red), 0.98 (NIR) and 0.98 (SWIR), respectively. To check the efficacy of the framework, the LISS-3 Deep SR product is also compared with closest acquisition Landsat-8 SR product and ground truth values obtained through vicarious calibration. The maximum relative deviation error found to be 1.34%, 1.82%, 3.25% and 2.16% for Green, Red, NIR and SWIR channels, respectively.

使用遥感图像进行地球观测是一种探索和评估地球上任何特定区域的地理资源的好奇方式。在这方面，印度 Resourcesat-2A (R2A) 遥感卫星利用其独特的三层成像机制在监测我们星球的关键资源方面发挥着重要作用。R2A 上的光学传感器具有良好的空间时间分辨率，适用于各种星载应用。大多数这些应用需要通过消除间歇性大气散射和吸收的影响来获得表面反射 (SR) 数据产品。辐射传输模型 (RTM) 用于执行计算密集型的大气校正，因此使用查找表 (LUT) 来插入中间值，作为精度和速度之间的权衡。然而，当使用足够大的 LUT 时，插值过程也变得非常计算密集。该论文提供了一种通过使用多层深度网络将插值建模为回归问题来消除这种权衡的方法。所提出的方法生成高度准确的深度 SR 产品，并显着减少周转时间。实验结果表明，与用于生成 R2A LISS-3 Deep SR 场景数据产品的传统基于插值的方法相比，使用开发的框架实现了 5 倍的加速。Deep SR 产品与纯 6SV 生成的产品进行比较，发现 R2 值分别为 0.97（绿色）、0.97（红色）、0.98（NIR）和 0.98（SWIR）。为了检查框架的有效性，LISS-3 Deep SR 产品还与最近采集的 Landsat-8 SR 产品和通过替代校准获得的地面真值进行了比较。绿色、红色、NIR 和 SWIR 通道的最大相对偏差误差分别为 1.34%、1.82%、3.25% 和 2.16%。