论文标题
超级分辨率卷积神经网络的有效性，从中等分辨率卫星图像增强土地覆盖分类
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images
论文作者
论文摘要
在现代世界中，卫星图像在森林管理和退化监测中起着关键作用。为了精确地量化林地覆盖的变化，必须使用空间良好的分辨率数据。自1972年以来，NASAS Landsat卫星提供覆盖地球每个角落的地面图像，事实证明，这是陆地变化分析的非常有用的资源，并已用于许多其他部门。但是，可以自由访问的卫星图像通常是中度至低分辨率的，这是分析精度的主要障碍。因此，我们进行了一项全面的研究，以证明我们的观点是，即使在既定的识别方法下，超分辨率卷积神经网络（SRCNN）通过超分辨率卷积神经网络（SRCNN）提高分辨率也将减少对像素的分类。我们测试了Sundarbans不同区域的原始Landsat-7图像及其高扫描版本，这些版本分别是双线性插值，双孔插值和SRCNN产生的，并且发现SRCNN以大量的数量胜过Srcnn。
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.
