Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

Abstract

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

References in corpus (3)

• Clearing the Skies: A deep network architecture for single-image rain removal
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• Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm

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• Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
• Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification
• Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification
• Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data
• Decision Tree Learning with Spatial Modal Logics