Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification

Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the population receptive field (pRF) in human visual cortex, we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network (Pixel DAG-RNN) to extract and apply spectral-spatial features for HSIs classification. In our model, an undirected cyclic graph (UCG) is used to represent the relevance connectivity of pixels in an image patch, and four DAGs are used to approximate the spatial relationship of UCGs. In order to avoid overfitting, weight sharing and dropout are adopted. The higher classification performance of our model on HSIs classification has been verified by experiments on three benchmark data sets.

Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification

Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification. In this paper, we propose a modified diversity of class probability estimation (MDCPE) with two deep neural networks to learn spectral-spatial feature for HSI classification. In co-training phase, recurrent neural network (RNN) and convolutional neural network (CNN) are utilized as two learners to extract features from labeled and unlabeled data. Based on the extracted features, MDCPE selects most credible samples to update initial labeled data by combining k-means clustering with the traditional diversity of class probability estimation (DCPE) co-training. In this way, MDCPE can keep new labeled data class-balanced and extract discriminative features for both the minority and majority classes. During testing process, classification results are acquired by co-decision of the two learners. Experimental results demonstrate that the proposed semi-supervised co-training method can make full use of unlabeled information to enhance generality of the learners and achieve favorable accuracies on all three widely used data sets: Salinas, Pavia University and Pavia Center.