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

## TL;DR

This paper introduces Pixel DAG-RNN, a novel neural network model inspired by human visual cortex, that effectively captures spectral and spatial features for hyperspectral image classification, outperforming existing methods on benchmark datasets.

## Contribution

The paper proposes a new Pixel DAG-RNN model utilizing DAGs to approximate pixel relevance in hyperspectral images, enhancing spectral-spatial feature extraction for improved classification accuracy.

## Key findings

- Achieved higher classification accuracy on three benchmark datasets.
- Effectively models spatial correlations using DAGs derived from UCGs.
- Reduces overfitting through weight sharing and dropout.

## Abstract

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.