Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images
This addresses the need for higher precision in forest management and degradation monitoring using freely available satellite data, though it is incremental as it applies an existing method to a specific domain.
The study tackled the problem of misclassification in land cover analysis from medium-resolution satellite images by enhancing resolution using a Super-Resolution Convolutional Neural Network (SRCNN), resulting in SRCNN significantly outperforming traditional interpolation methods like bilinear and bicubic on LANDSAT-7 images of the Sundarbans.
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.