High-resolution remote sensing scene classification (HR-RSSC) plays an increasingly important role since it aims to enhance the scene semantic understanding. Recently, convolutional neural networks (CNNs) proved their effectiveness in learning powerful feature representations for various visual recognition tasks. However, in the RS domain, the performance of CNN is still limited due to the lack of sufficient labeled data. In this letter, we propose an HR-RSSC method based on CNN transfer learning (TL) for feature extraction (FE) and deep forest (DF) for classification. In fact, we extract deep features from the last convolutional layer in order to avoid the use of the fully connected layers (FCLs) which need many parameters to tune. Moreover, we train a DF model that is based on ensemble learning that can achieve better performances than single classifiers and is easy to train with few parameters. We evaluate the proposed method on two RS image data sets. Compared to full-training, fine-tuning, and state-of-the-art CNN TL methods, the results demonstrate the effectiveness of the DF model for HR-RSSC based on CNN TL in terms of overall accuracy and training time.

In this paper, we propose a novel adaptive band selection approach for hyperspectral image semantic interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image semantic interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the semantic interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy.