• Mandelli Karthik
• Devagiri Purna Sai Kumar Reddy
• Pavan Tanneru

Satellite image classification plays a crucial role in various domains including agriculture, urban planning, disaster response, and environmental monitoring. However, it often relies heavily on large manually labeled datasets for accurate classification. In this research paper, we propose a novel method that combines Convolutional Neural Networks (CNNs) with Inverse Reinforcement Learning (IRL) to alleviate the burden of labeling while enhancing classification accuracy.

Image classification,Convolutional Neural Networks (CNNs),Inverse Reinforcement Learning (IRL), Deep learning,Data labeling,Feature extraction,Pattern recognition,Data scarcity

"Satellite Image Classification using cnn and irl", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a759-a769, April-2024, Available :http://www.jetir.org/papers/JETIR2404096.pdf

"Satellite Image Classification using cnn and irl", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa759-a769, April-2024, Available at : http://www.jetir.org/papers/JETIR2404096.pdf