  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.
