Real-time progressive hyperspectral image processing: Endmember finding and anomaly detection
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[New York, NY]
Springer Science+Business Media
2016
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| Schlagworte: | |
| Online-Zugang: | BTU01 FAB01 FAW01 FHA01 FHI01 FHN01 FHR01 FKE01 FRO01 FWS01 FWS02 UBY01 URL des Erstveröffentlichers Inhaltsverzeichnis Abstract |
| Beschreibung: | 1 Online-Ressource (xix, 623 p. 331 illus., 256 illus. in color) |
| ISBN: | 9781441961877 |
| DOI: | 10.1007/978-1-4419-6187-7 |
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| adam_text | REAL-TIME PROGRESSIVE HYPERSPECTRAL IMAGE PROCESSING
/ CHANG, CHEIN-I.
: 2016
TABLE OF CONTENTS / INHALTSVERZEICHNIS
OVERVIEW AND INTRODUCTION
PART I: PRELIMINARIES
LINEAR SPECTRAL MIXTURE ANALYSIS
FINDING ENDMEMBERS IN HYPERSPECTRAL IMAGERY
LINEAR SPECTRAL UNMIXING WITH THREE CRITERIA, LEAST SQUARES ERROR,
SIMPLEX VOLUME AND ORTHOGONAL PROJECTION
HYPERSPECTRAL TARGET DETECTION
PART II: SAMPLE-WISE SEQUENTIAL PROCESSES FOR FINDING ENDMEMBERS
ABUNDANCE-UNCONSTRAINED SEQUENTIAL ENDMEMBER FINDING ALGORITHMS:
ORTHOGONAL PROJECTION
FULLY ABUNDANCE-CONSTRAINED SEQUENTIAL ENDMEMBER FINDING ALGORITHMS:
SIMPLEX VOLUME ANALYSIS
PARTIALLY ABUNDANCE NON-NEGATIVITY-CONSTRAINED ENDMEMBER FINDING
ALGORITHMS: CONVEX CONE VOLUME ANALYSIS
FULLY ABUNDANCE-CONSTRAINED SEQUENTIAL LINEAR SPECTRAL MIXTURE ANALYSIS
FOR FINDING ENDMEMBERS
PART III: SAMPLE-WISE PROGRESSIVE PROCESSES FOR FINDING ENDMEMBERS
ABUNDANCE-UNCONSTRAINED PROGRESSIVE ENDMEMBER FINDING ALGORITHMS:
ORTHOGONAL PROJECTION
FULLY ABUNDANCE-UNCONSTRAINED PROGRESSIVE ENDMEMBER FINDING ALGORITHMS:
SIMPLEX VOLUME ANALYSIS
PARTIALLY ABUNDANCE NON-NEGATIVITY-CONSTRAINED PROGRESSIVE ENDMEMBER
FINDING ALGORITHMS: CONVEX CONE VOLUME ANALYSIS
SULLY ABUNDANCE-CONSTRAINED PROGRESSIVE LINEAR SPECTRAL MIXTURE ANALYSIS
FOR FINDING ENDMEMBERS
PART IV: SAMPLE-WISE PROGRESSIVE UNSUPERVISED TARGET DETECTION
PROGRESSIVE ANOMALY DETECTION
PROGRESSIVE ADAPTIVE ANOMALY DETECTION
PROGRESSIVE WINDOW-BASED ANOMALY DETECTION
PROGRESSIVE SUBPIXEL TARGET DETECTION AND CLASSIFICATION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
REAL-TIME PROGRESSIVE HYPERSPECTRAL IMAGE PROCESSING
/ CHANG, CHEIN-I.
: 2016
ABSTRACT / INHALTSTEXT
THE BOOK COVERS THE MOST CRUCIAL PARTS OF REAL-TIME HYPERSPECTRAL IMAGE
PROCESSING: CAUSALITY AND REAL-TIME CAPABILITY. RECENTLY, TWO NEW
CONCEPTS OF REAL TIME HYPERSPECTRAL IMAGE PROCESSING, PROGRESSIVE
HYPERSPECTRAL IMAGING (PHSI) AND RECURSIVE HYPERSPECTRAL IMAGING (RHSI).
BOTH OF THESE CAN BE USED TO DESIGN ALGORITHMS AND ALSO FORM AN INTEGRAL
PART OF REAL TIME HYPERPSECTRAL IMAGE PROCESSING. THIS BOOK FOCUSES ON
PROGRESSIVE NATURE IN ALGORITHMS ON THEIR REAL-TIME AND CAUSAL
PROCESSING IMPLEMENTATION IN TWO MAJOR APPLICATIONS, ENDMEMBER FINDING
AND ANOMALY DETECTION, BOTH OF WHICH ARE FUNDAMENTAL TASKS IN
HYPERSPECTRAL IMAGING BUT GENERALLY NOT ENCOUNTERED IN MULTISPECTRAL
IMAGING. THIS BOOK IS WRITTEN TO PARTICULARLY ADDRESS PHSI IN REAL TIME
PROCESSING, WHILE A BOOK, RECURSIVE HYPERSPECTRAL SAMPLE AND BAND
PROCESSING: ALGORITHM ARCHITECTURE AND IMPLEMENTATION (SPRINGER 2016)
CAN BE CONSIDERED AS ITS COMPANION BOOK. INCLUDES PRELIMINARY BACKGROUND
WHICH IS ESSENTIAL TO THOSE WHO WORK IN HYPERSPECTRAL IMAGING AREA
DEVELOPS SEQUENTIAL AND PROGRESSIVE ALGORITHMS FOR FINDING ENDMEMBERS AS
THEY RELATE TO REAL TIME HYPERSPECTRAL IMAGE PROCESSING DESIGNS
ALGORITHMS FOR ANOMALY DETECTION FROM CAUSALITY AND REAL TIME
PERSPECTIVES AND INVESTIGATES THE EFFECTS OF CAUSALITY AND REAL-TIME
PROCESSING IN ANOMALY DETECTION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Chang, Chein-I |
| author_GND | (DE-588)1080929193 |
| author_facet | Chang, Chein-I |
| author_role | aut |
| author_sort | Chang, Chein-I |
| author_variant | c i c cic |
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| collection | ZDB-2-ENG |
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| dewey-full | 621.382 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
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| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1007/978-1-4419-6187-7 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-08-01T12:13:37Z |
| institution | BVB |
| isbn | 9781441961877 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028919971 |
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| open_access_boolean | |
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| publishDate | 2016 |
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| publisher | Springer Science+Business Media |
| record_format | marc |
| spellingShingle | Chang, Chein-I Real-time progressive hyperspectral image processing Endmember finding and anomaly detection Engineering Image processing Pattern recognition Biometrics (Biology) Signal, Image and Speech Processing Image Processing and Computer Vision Pattern Recognition Biometrics Ingenieurwissenschaften |
| title | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection |
| title_auth | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection |
| title_exact_search | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection |
| title_full | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection Chein-I Chang |
| title_fullStr | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection Chein-I Chang |
| title_full_unstemmed | Real-time progressive hyperspectral image processing Endmember finding and anomaly detection Chein-I Chang |
| title_short | Real-time progressive hyperspectral image processing |
| title_sort | real time progressive hyperspectral image processing endmember finding and anomaly detection |
| title_sub | Endmember finding and anomaly detection |
| topic | Engineering Image processing Pattern recognition Biometrics (Biology) Signal, Image and Speech Processing Image Processing and Computer Vision Pattern Recognition Biometrics Ingenieurwissenschaften |
| topic_facet | Engineering Image processing Pattern recognition Biometrics (Biology) Signal, Image and Speech Processing Image Processing and Computer Vision Pattern Recognition Biometrics Ingenieurwissenschaften |
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