Scene interpretation of remote sensing images entails effective spatial feature information extraction and application of an appropriate pattern recognition algorithm for feature learning. In literature, state-of-the-art results are attained in remote sensing using pre-trained convolutional neural networks (convNets or CNNs) for transfer learning in deep feature extraction and then applying classifiers to learn the features for scene classification. This work proposes a method that utilizes VGG-16 model for feature extraction and the multi-grain forest for feature learning and classification with ensemble classifiers majority voting. The Effectiveness of the proposed method is evaluated with UCMerced and WHU-Siri public datasets. Improved classification results are attained with the proposed method as compared to methods in the literature.