A
CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image
Classification
Abstract:
Recently, researchers have shown the powerful
ability of deep methods with multilayers to extract high-level features and to
obtain better performance for hyperspectral image classification. However, a
common problem of traditional deep models is that the learned deep models might
be suboptimal because of the limited number of training samples, especially for
the image with large intraclass variance and low interclass variance. In this
paper, novel convolutional neural networks (CNNs) with multiscale convolution
(MS-CNNs) are proposed to address this problem by extracting deep multiscale features
from the hyperspectral image. Moreover, deep metrics usually accompany with
MS-CNNs to improve the representational ability for the hyperspectral image.
However, the usual metric learning would make the metric parameters in the
learned model tend to behave similarly. This similarity leads to obvious
model's redundancy and, thus, shows negative effects on the description ability
of the deep metrics. Traditionally, determinantal point process (DPP) priors,
which encourage the learned factors to repulse from one another, can be imposed
over these factors to diversify them. Taking advantage of both the MS-CNNs and
DPP-based diversity-promoting deep metrics, this paper develops a CNN with
multiscale convolution and diversified metric to obtain discriminative features
for hyperspectral image classification. Experiments are conducted over four
real-world hyperspectral image data sets to show the effectiveness and
applicability of the proposed method. Experimental results show that our method
is better than original deep models and can produce comparable or even better
classification performance in different hyperspectral image data sets with
respect to spectral and spectral-spatial features.
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