NDLI: Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class

Content Provider	IEEE Xplore Digital Library
Author	Qunming Wang Wenzhong Shi Liguo Wang
Copyright Year	1980
Abstract	There is a type of algorithm for subpixel mapping (SPM), namely, the soft-then-hard SPM (STHSPM) algorithm that first estimates soft attribute values for land cover classes at the subpixel scale level and then allocates classes (i.e., hard attribute values) for subpixels according to the soft attribute values. This paper presents a novel class allocation approach for STHSPM algorithms, which allocates classes in units of class (UOC). First, a visiting order for all classes is predetermined, and the number of subpixels belonging to each class is calculated using coarse fraction data. Then, according to the visiting order, the subpixels belonging to the being visited class are determined by comparing the soft attribute values of this class, and the remaining subpixels are used for the allocation of the next class. The process is terminated when each subpixel is allocated to a class. UOC was tested on three remote sensing images with five STHSPM algorithms: back-propagation neural network, Hopfield neural network, subpixel/pixel spatial attraction model, kriging, and indicator cokriging. UOC was also compared with three existing allocation methods, i.e., linear optimization technique (LOT), sequential assignment in units of subpixel (UOS), and a method that assigns subpixels with highest soft attribute values first (HAVF). Results show that for all STHSPM algorithms, UOC is able to produce higher SPM accuracy than UOS and HAVF; compared with LOT, UOC is able to achieve at least comparable accuracy but needs much less computing time. Hence, UOC provides an effective and real-time class allocation method for STHSPM algorithms.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	2940
Ending Page	2959
Page Count	20
File Size	4015086
File Format	PDF
ISSN	01962892
Volume Number	52
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resource management Spatial resolution Vectors Optimization Training Remote sensing Mathematical model superresolution mapping Class allocation image classification subpixel mapping (SPM) subpixel sharpening
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Class Allocation for Soft-Then-Hard Subpixel Mapping Algorithms With Adaptive Visiting Order of Classes

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Subpixel Land Cover Mapping by Integrating Spectral and Spatial Information of Remotely Sensed Imagery

Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information

Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation

Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach

A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

Superresolution Mapping Using Multiple Dictionaries by Sparse Representation

Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class

Similar Documents

Class Allocation for Soft-Then-Hard Subpixel Mapping Algorithms With Adaptive Visiting Order of Classes

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Subpixel Land Cover Mapping by Integrating Spectral and Spatial Information of Remotely Sensed Imagery

Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information

Utilizing Multiple Subpixel Shifted Images in Subpixel Mapping With Image Interpolation

Subpixel Mapping Using Markov Random Field With Multiple Spectral Constraints From Subpixel Shifted Remote Sensing Images

Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach

A New Spatial Attraction Model for Improving Subpixel Land Cover Classification

Superresolution Mapping Using Multiple Dictionaries by Sparse Representation

Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class