Assessing the Classification Accuracy of Multisource Remote Sensing Data: Fid-Bau Portal

Fachinformationsdienst BAUdigital

A platform for research: civil engineering, architecture and urbanism

Assessing the Classification Accuracy of Multisource Remote Sensing Data

Fitzgerald, R.W.

Access

Check availability in my library

Order at Subito €

Page navigation

Document information

Export, share and cite

Assessing the Classification Accuracy of Multisource Remote Sensing Data

Fitzgerald, R.W.

Assessing the Classification Accuracy of Multisource Remote Sensing Data

Fitzgerald, R.W.

Access

Check availability in my library

Order at Subito €

Page navigation

Document information

Export, share and cite

Document information

Title:

Assessing the Classification Accuracy of Multisource Remote Sensing Data

Contributors:

Fitzgerald, R.W. (author) / Lees, B.G.

Published in:

Remote sensing of environment ; 47

Publication date:

1994

ISSN:

Type of media:

Article (Journal)

Type of material:

Print

Language:

English

Keywords:

Erde , Umweltveränderung , Fernerkundung , Umweltforschung , Umweltüberwachung , Umweltschaden

Classification:

BKL: 43.03 Methoden der Umweltforschung und des Umweltschutzes / 38.03 Methoden und Techniken der Geowissenschaften / 74.41 Luftaufnahmen, Photogrammetrie

Similar titles

Fuzzy Contextual Classification of Multisource Remote Sensing Images

Binaghi, E. | Online Contents | 1997

LETTERS - Multisource Remote Sensing Data Classification Based on Consensus and Pruning

Benediktsson, J.A. | Online Contents | 2003

Fusion Approaches to Individual Tree Species Classification Using Multisource Remote Sensing Data

Qian Li / Baoxin Hu / Jiali Shang et al. | DOAJ | 2023

Free access

Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

Camps-Valls, G. | Online Contents | 2008

Assessing heat vulnerability risk of Jinan and Guangzhou's older populations based on multisource remote sensing data

Chen, Jifei / Shi, Xiaoming / Shi, Yongying et al. | Elsevier | 2025