Granular space, knowledge-encoded deep learning architecture and remote sensing image classification

Abstract

Hand-crafted features of remotely sensed (RS) image require the involvement of expensive human experts for classification. This factor motivates for designing the classification model with representative feature learning-based deep architecture to automate the feature extraction process and improve the generalization capability of the model. With this reasoning, we propose a deep auto-encoder neural network (NN) architecture with knowledge-encoded granular space for the classification of RS images. The network works with wavelet-rough granulated spaces and its architecture is designed with the encoded domain knowledge that strategically initializes the network parameters. Mostly, the learning time and performance of deep auto-encoders are persuaded by randomly selected weights and thus, we aim here to minimize these efforts with the domain knowledge. Neighborhood rough sets (NRS) are used to encode the domain knowledge and explore the contextual information for improved decision. We perform the knowledge-encoding operation for all stages of the auto-encoder. The proposed model thus exploits the mutual merits of deep network, wavelet-rough granular space and knowledge-encoding method. Comparative experimental results with multispectral and hyperspectral RS images demonstrate the superiority of our model to the related advanced methods.