Lightweight Spectral-Spatial Squeeze-and-Excitation Residual Bag-of-Features Learning for Hyperspectral Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10246262" target="_blank" >RIV/61989100:27240/20:10246262 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8976424" target="_blank" >https://ieeexplore.ieee.org/document/8976424</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TGRS.2019.2961681" target="_blank" >10.1109/TGRS.2019.2961681</a>

Result language

angličtina

Original language name

Lightweight Spectral-Spatial Squeeze-and-Excitation Residual Bag-of-Features Learning for Hyperspectral Classification

Original language description

Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral-spatial domain. In this article, we design an end-to-end spectral-spatial squeeze-and-excitation (SE) residual bag-of-feature (S3EResBoF) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial-spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: A Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Geoscience and Remote Sensing

ISSN

0196-2892

e-ISSN

—

Volume of the periodical

58

Issue of the periodical within the volume

8

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

5277-5290

UT code for WoS article

000552371900004

EID of the result in the Scopus database

2-s2.0-85086138500