Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs) recently. However, most of these models only input the whole spectral bands into RNNs directly, which may not fully explore the specific properties of HSIs. In this paper, we propose a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of HSIs. It mainly consists of two RNN layers. The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands. To improve the discriminative ability of the learned features, we design two strategies for the proposed model. Besides, considering the rich spatial information contained in HSIs, we further extend the proposed model to its spectral-spatial counterpart by incorporating some convolutional layers. To test the effectiveness of our proposed models, we conduct experiments on two widely used HSIs. The experimental results show that our proposed models can achieve better results than the compared models

Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with complex image patterns. However, the rich spatial information still remains unexploited, since most of the deep learning algorithms only focus on small image patches that overlook the contextual information at larger scales. To utilize these contextual information and improve the classification performance for high-resolution imagery, we propose a graph-based model in order to capture the contextual information over semantic segments of the image. First, we explore semantic segments which build on the top of deep features and obtain the initial classification result. Then, we further improve the initial classification results with a higher-order co-occurrence model by extending the existing conditional random field (HCO-CRF) algorithm. Compared to the pixel- and object-based CNN methods, the proposed model achieved better performance in terms of classification accuracy

Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with complex image patterns. However, the rich spatial information still remains unexploited, since most of the deep learning algorithms only focus on small image patches that overlook the contextual information at larger scales. To utilize these contextual information and improve the classification performance for high-resolution imagery, we propose a graph-based model in order to capture the contextual information over semantic segments of the image. First, we explore semantic segments which build on the top of deep features and obtain the initial classification result. Then, we further improve the initial classification results with a higher-order co-occurrence model by extending the existing conditional random field (HCO-CRF) algorithm. Compared to the pixel- and object-based CNN methods, the proposed model achieved better performance in terms of classification accuracy