2 research outputs found
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
Recently, convolutional neural network (CNN)-based methods are proposed for
hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as
the deep image prior (DIP) have received much attention because these methods
do not require any training data. However, DIP suffers from the
semi-convergence behavior, i.e., the iteration of DIP needs to terminate by
referring to the ground-truth image at the optimal iteration point. In this
paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for
HSI mixed noise removal. Specifically, we incorporate DIP with a
spatial-spectral total variation (SSTV) term to fully preserve the
spatial-spectral local smoothness of the HSI and an -norm term to
capture the complex sparse noise. The proposed S2DIP jointly leverages the
expressive power brought from the deep CNN without any training data and
exploits the HSI and noise structures via hand-crafted priors. Thus, our method
avoids the semi-convergence behavior, showing higher stabilities than DIP.
Meanwhile, our method largely enhances the HSI denoising ability of DIP. To
tackle the proposed denoising model, we develop an alternating direction
multiplier method algorithm. Extensive experiments demonstrate that the
proposed S2DIP outperforms optimization-based and supervised CNN-based
state-of-the-art HSI denoising methods
Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification
A central problem in hyperspectral image classification is obtaining high
classification accuracy when using a limited amount of labelled data. In this
paper we present a novel graph-based framework, which aims to tackle this
problem in the presence of large scale data input. Our approach utilises a
novel superpixel method, specifically designed for hyperspectral data, to
define meaningful local regions in an image, which with high probability share
the same classification label. We then extract spectral and spatial features
from these regions and use these to produce a contracted weighted
graph-representation, where each node represents a region rather than a pixel.
Our graph is then fed into a graph-based semi-supervised classifier which gives
the final classification. We show that using superpixels in a graph
representation is an effective tool for speeding up graphical classifiers
applied to hyperspectral images. We demonstrate through exhaustive quantitative
and qualitative results that our proposed method produces accurate
classifications when an incredibly small amount of labelled data is used. We
show that our approach mitigates the major drawbacks of existing approaches,
resulting in our approach outperforming several comparative state-of-the-art
techniques.Comment: 11 page