• SAMRITI HEER
• Dr. Jitendra Sheetlani

Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinary camera. Curiously, we find transfer learning from models trained on larger, more generic natural images datasets outperformed transfer learning from models trained directly on smaller remotely sensed datasets. Nonetheless, results show that transfer learning provides a powerful tool for remote-sensing scene classification

CNN, Learning, Remote,Sensing,Image, Classification, learning, classification

"ANALYTICAL STUDY ON CNN BASED REMOTE SENSING DATA CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.147-152, March-2019, Available :http://www.jetir.org/papers/JETIR1903L21.pdf

"ANALYTICAL STUDY ON CNN BASED REMOTE SENSING DATA CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp147-152, March-2019, Available at : http://www.jetir.org/papers/JETIR1903L21.pdf