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.

遥感（RS）图像的手工制作功能需要昂贵的人类专家参与分类。这个因素促使设计具有代表性的，基于特征学习的深度架构的分类模型，以自动进行特征提取过程并提高模型的泛化能力。通过这种推理，我们提出了一种具有知识编码粒度空间的深层自动编码器神经网络（NN）体系结构，用于RS图像分类。该网络适用于小波粗糙的粒度空间，其架构是通过编码域知识设计的，该知识可以战略性地初始化网络参数。大多数情况下，深度自动编码器的学习时间和性能可以通过随机选择的权重来说服，因此，我们的目标是通过领域知识来最大程度地减少这些工作。邻域粗糙集（NRS）用于对领域知识进行编码，并探索上下文信息以改进决策。我们对自动编码器的所有阶段执行知识编码操作。因此，该模型利用了深层网络，小波粗糙粒度空间和知识编码方法的优点。多光谱和高光谱RS图像的对比实验结果表明，我们的模型优于相关的先进方法。小波粗糙粒度空间和知识编码方法。多光谱和高光谱RS图像的对比实验结果表明，我们的模型优于相关的先进方法。小波粗糙粒度空间和知识编码方法。多光谱和高光谱RS图像的对比实验结果表明，我们的模型优于相关的先进方法。