2 research outputs found
Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field
To improve the classification performance in the context of hyperspectral
image processing, many works have been developed based on two common
strategies, namely the spatial-spectral information integration and the
utilization of neural networks. However, both strategies typically require more
training data than the classical algorithms, aggregating the shortage of
labeled samples. In this letter, we propose a novel framework that organically
combines the spectrum-based deep metric learning model and the conditional
random field algorithm. The deep metric learning model is supervised by the
center loss to produce spectrum-based features that gather more tightly in
Euclidean space within classes. The conditional random field with Gaussian edge
potentials, which is firstly proposed for image segmentation tasks, is
introduced to give the pixel-wise classification over the hyperspectral image
by utilizing both the geographical distances between pixels and the Euclidean
distances between the features produced by the deep metric learning model. The
proposed framework is trained by spectral pixels at the deep metric learning
stage and utilizes the half handcrafted spatial features at the conditional
random field stage. This settlement alleviates the shortage of training data to
some extent. Experiments on two real hyperspectral images demonstrate the
advantages of the proposed method in terms of both classification accuracy and
computation cost
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in
hyperspectral image classification. However, the general deep learning methods
mainly take advantage of the information of sample itself or the pairwise
information between samples while ignore the intrinsic data structure within
the whole data. To tackle this problem, this work develops a novel deep
manifold embedding method(DMEM) for hyperspectral image classification. First,
each class in the image is modelled as a specific nonlinear manifold and the
geodesic distance is used to measure the correlation between the samples. Then,
based on the hierarchical clustering, the manifold structure of the data can be
captured and each nonlinear data manifold can be divided into several
sub-classes. Finally, considering the distribution of each sub-class and the
correlation between different subclasses, the DMEM is constructed to preserve
the estimated geodesic distances on the data manifold between the learned low
dimensional features of different samples. Experiments over three real-world
hyperspectral image datasets have demonstrated the effectiveness of the
proposed method.Comment: Accepted by IEEE TCY