  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.
