In the modern world, satellite images play a key role in forest management and degradation monitoring. For a precise quantification of forest land cover changes, the availability of spatially fine resolution data is a necessity. Since 1972, NASAs LANDSAT Satellites are providing terrestrial images covering every corner of the earth, which have been proved to be a highly useful resource for terrestrial change analysis and have been used in numerous other sectors. However, freely accessible satellite images are, generally, of medium to low resolution which is a major hindrance to the precision of the analysis. Hence, we performed a comprehensive study to prove our point that, enhancement of resolution by Super-Resolution Convolutional Neural Network (SRCNN) will lessen the chance of misclassification of pixels, even under the established recognition methods. We tested the method on original LANDSAT-7 images of different regions of Sundarbans and their upscaled versions which were produced by bilinear interpolation, bicubic interpolation, and SRCNN respectively and it was discovered that SRCNN outperforms the others by a significant amount.
翻译:在现代世界,卫星图像在森林管理和退化监测方面发挥着关键作用。为了准确量化森林土地覆盖变化,有必要提供空间精细分辨率数据。自1972年以来,美国航天局LANDSAT卫星正在提供覆盖地球每个角落的地面图像,这些图像已证明是进行地面变化分析的非常有用的资源,并被用于许多其他部门。然而,免费获得的卫星图像一般是中低分辨率的,这是分析精确度的一大障碍。因此,我们进行了一项全面研究,以证明我们的观点,即超级分辨率革命神经网络(SRCNN)加强分辨率将减少将像素进行错误分类的可能性,即使在既定的识别方法下也是如此。我们测试了Sundarbans不同地区原LANDSAT-7图像及其升级版的方法,这些方法分别由双线间分解、双线间解和SRCNNE制作,发现SRCNN(SRCNN)将大大超出其他图像。