NDLI: Improved neural network classification of hyperspectral imagery using weighted genetic algorithm and hierarchical segmentation

Content Provider	IET Digital Library
Author	Akbari, Davood
Abstract	This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. The spatial information is obtained by an enhanced marker-based hierarchical segmentation (MHS) algorithm. The weighted genetic (WG) algorithm is first employed to obtain the subspace of hyperspectral data. The obtained features are then fed into the multi-layer perceptron (MLP) neural network classification algorithm. Afterwards, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, namely MLP-MHS, the markers are extracted from the classification maps obtained by MLP and support vector machine classifiers. Experiments on two benchmark hyperspectral datasets, Pavia University and Berlin, validate the soundness of the proposed approach compared to the MLP and the original MHS algorithms.
Starting Page	2169
Ending Page	2175
Page Count	7
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5693
Journal	IET Image Processing
Publisher Date	2019-08-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benchmark Hyperspectral Datasets Classification Maps Computer Vision And Image Processing Technique Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Enhanced Marker-based Hierarchical Segmentation Algorithm Feature Extraction Genetic Algorithm Geophysical Image Processing Geophysical Technique And Equipment Geophysics Computing Hyperspectral Data Hyperspectral Image Hyperspectral Imagery Image Classification Image Recognition Image Segmentation Improved Neural Network Classification Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Knowledge Engineering Technique MHS Algorithm MLP-MHS Modified Spectral–spatial Classification Approach Multilayer Perceptron Neural Network Classification Algorithm Multilayer Perceptrons Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Optimisation Technique Original MHS Algorithm Pattern Classification Spatial Information Statistics Support Vector Machine Weighted Genetic Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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