The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has assumed an important role in a wide range of applications such as urban planning, natural hazards detection, environmental monitoring, vegetation mapping or geospatial object detection. During the past years, the research community focusing on RSISC tasks has shown significant effort to publish diverse datasets as well as to propose different approaches. Recently, almost all proposed RSISC systems are based on deep learning models, which proves powerful and outperform traditional approaches using image processing and machine learning. In this paper, we also leverage the power of deep learning technologies, evaluate a variety of deep neural network architectures and indicate main factors affecting the performance of a RSISC system. Given the comprehensive analysis, we propose a deep learning based framework for RSISC, which makes use of a transfer learning technique and a multihead attention scheme. The proposed deep learning framework is evaluated on the NWPU-RESISC45 benchmark dataset and achieves a classification accuracy of up to 92.6% and 94.7% with two official data split suggestions (10% and 20% of entire the NWPU-RESISC45 dataset for training). The achieved results are very competitive and show potential for real-life applications.