English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In the modern world, satellite images play a key role in forest managementand degradation monitoring. For a precise quantification of forest land coverchanges, the availability of spatially fine resolution data is a necessity.Since 1972, NASAs LANDSAT Satellites are providing terrestrial images coveringevery corner of the earth, which have been proved to be a highly usefulresource for terrestrial change analysis and have been used in numerous othersectors. However, freely accessible satellite images are, generally, of mediumto low resolution which is a major hindrance to the precision of the analysis.Hence, we performed a comprehensive study to prove our point that, enhancementof resolution by Super-Resolution Convolutional Neural Network (SRCNN) willlessen the chance of misclassification of pixels, even under the establishedrecognition methods. We tested the method on original LANDSAT-7 images ofdifferent regions of Sundarbans and their upscaled versions which were producedby bilinear interpolation, bicubic interpolation, and SRCNN respectively and itwas discovered that SRCNN outperforms the others by a significant amount.

Effectivity of super resolution convolutional neural network for the  enhancement of land cover classification from medium resolution satellite  images