Hyperspectral images usually have small number of labeled samples because of the labeling cost and process difficulty. In conventional classification algorithms, classifier performance is depending on the number training samples. In this study, a deep learning based semi-supervised learning framework is proposed to solve this limited labeled sample size problem by utilizing power of labeled and unlabeled samples. The main aim of the study is constructing a general purpose deep model for a specific hyperspectral sensor type and using the model with little effort for all data sets obtained from this sensor type. In proposed framework, the model is trained with a data set from a hyperspectral sensor subsequently it is fine-tuned and evaluated with another data set acquired from the same sensor. Linearly separable deep features of the evaluation data set are extracted from the fine-tuned general deep model. Additionally, a new data formation method is proposed in the transition from hyperspectral data sub-cubes to the deep neural network input. Besides that, three different clustering methods have been used for selecting the initial labeled samples in the semi-supervised learning phase to observe the effects of the sample selection comparatively. As an another contribution of the study, a new semi-supervised sparse representation classifier (S3 RC) is proposed with labeled and unlabeled sample information by using linearly separable deep features. The performance of the proposed framework is proven by the experimental results with using small sample sizes. (c) 2020 Elsevier B.V. All rights reserved.