Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
This work addresses classification accuracy for hyperspectral images, which is an incremental improvement in a domain-specific application.
The paper tackled hyperspectral image classification by proposing a Pixel DAG-RNN model that leverages spatial correlation inspired by the human visual cortex, achieving higher classification performance on three benchmark datasets.
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.