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Hybrid Dilated Convolution Guided Feature Filtering and Enhancement Strategy for Hyperspectral Image Classification

2021-12-30

 

Author(s): Liu, RN (Liu, Runmin); Cai, WW (Cai, Weiwei); Li, GJ (Li, Guangjun); Ning, X (Ning, Xin); Jiang, YZ (Jiang, Yizhang)

Source: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS DOI: 10.1109/LGRS.2021.3100407 Early Access Date: AUG 2021

Abstract: With the increasing maturity of optics and photonics, hyperspectral technology has also greatly advanced. Hyperspectral images composed of hundreds of adjacent bands and containing useful information can be easily obtained. However, unlike ordinary remote sensing images, each sample in hyperspectral remote sensing images has high-dimensional features and contains rich spatial and spectral information, which greatly increases the difficulty of feature selection and mining, increases the computational complexity, and limits the recognition accuracy of the model. Therefore, in this letter, a novel hybrid dilated-convolution-guided feature filtering and enhancement strategy (HDCFE-Net) model is proposed to classify hyperspectral images. Dilated convolution can reduce the spatial feature loss without reducing the receptive field and can obtain distant features. It can also be combined with the traditional convolution without losing its original information. We propose a feature filtering and enhancement strategy that eliminates redundant features and reduces computational complexity. The core concept is to set a threshold feature value, like the rounding method, to filter and enhance features. Experiments on three well-known hyperspectral datasets--Indian Pines (IPs), Pavia University (PU), and Salinas--show that in less than 1% (IPs: 5%) of the training samples, the overall accuracy (OA) of our method is 77%, 89%, and 91%, respectively, which is superior to several well-known methods. The experiments demonstrated the effectiveness and superiority of HDCFE-Net.

Accession Number: WOS:000732353300001

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

Cai, Weiwei         AAH-5456-2020         0000-0001-6795-6152

ISSN: 1545-598X

eISSN: 1558-0571

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