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Expansion of LISS III swath using AWiFS wider swath data and contourlet coefficients learning

机译：使用AWiFS扩展LISS III测绘带更广泛的测绘带数据和Contourlet系数学习

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摘要

Spaceborne sensors have limited capability to acquire images with wider swath at high spatial and high temporal resolutions simultaneously. This study reports a ground processing technique that combines images from two sensors onboard Resourcesat-2 (RS2) Linear Imaging and Self-Scanning Sensor (LISS III) and an Advanced Wide-Field Sensor (AWiFS) to overcome this limitation. The spatial resolution of LISS III is 23.5x23.5m and that of AWiFS is 56x56m. The temporal resolution of LISS III is 24days and that of AWiFS is 5days. The 140-km swath of the LISS III overlaps at center portion of 740-km swath of AWiFS in simultaneous acquisition. Assume that the nonoverlapping region of the AWiFS contains similar Earth's surface features of the LISS III overlapping region; then, it is possible to enhance the spatial resolution of AWiFS to the spatial resolution of LISS III in the nonoverlapping region. With this assumption, we propose a novel technique to enhance the spatial resolution of the nonoverlapping region through a single-image super-resolution technique using nonsubsampled contourlet transform (NSCT) and evaluated it on RS2 data-sets. The proposed method can create a synthetic image with 740-km swath at 23.5x23.5m spatial and 5-day temporal resolutions. Experimental results demonstrated that it outperforms the support vector regression (SVR)-based methods in prediction accuracy and computational time.
机译：星载传感器具有有限的能力，无法同时以高空间和高时间分辨率采集具有较宽幅幅的图像。这项研究报告了一种地面处理技术，该技术结合了来自Resourcesat-2（RS2）线性成像和自扫描传感器（LISS III）上的两个传感器和高级广域传感器（AWiFS）的图像，以克服这一限制。 LISS III的空间分辨率为23.5x23.5m，而AWiFS的空间分辨率为56x56m。 LISS III的时间分辨率为24天，而AWiFS的时间分辨率为5天。在同时采集时，LISS III的140公里宽幅与AWiFS 740公里宽幅的中心部分重叠。假设AWiFS的非重叠区域包含与LISS III重叠区域相似的地球表面特征；然后，有可能将AWiFS的空间分辨率提高到非重叠区域中LISS III的空间分辨率。有了这个假设，我们提出了一种新技术，通过使用非下采样轮廓波变换（NSCT）的单图像超分辨率技术来增强非重叠区域的空间分辨率，并在RS2数据集上对其进行了评估。所提出的方法可以在23.5x23.5m的空间和5天的时间分辨率下创建具有740公里长的合成图像。实验结果表明，该方法在预测准确性和计算时间方面优于基于支持向量回归（SVR）的方法。

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