Analysis of HYDICE data for information fusion in cartographic feature extraction

Ford, Stephen J.; McGlone, J. Chris; Cochran, Steven Douglas; Shufelt, Jefferey A.; Harvey, Wilson A.; McKeown, David M.

doi:10.1109/igarss.1998.702324

Cited by 6 publications

(2 citation statements)

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“…Researches were done for ordinary remote sensing images as well as fused or merged images. Ford et al (1998) analysed the HYDICE Data for Information Fusion in Cartographic Feature Extraction. Huertas et al (1999) fused HYDICE images with conventional PAN images for automated building extraction and modelling.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploiting IKONOS and Hyperion data fusion for automated road extraction

Tiwari

Pande

Aye³

2010

Geocarto International

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We present here the examples that show how fusing data from hyperspectral sensors with data from high spatial resolution sensors can enhance overall road detection accuracy. The fusion of hyperspectral and high spatial resolution data combines their superior respective spectral and spatial information. IKONOS (MSS) and Hyperion images were fused using the principal component analysis (PCA) method. The approach for road extraction integrates multiresolution segmentation and object oriented classification. Road extraction is done from an IKONOS (MSS) image and a Hyperion and IKONOS (MSS) merged image and comparisons are made depending on accuracy and quality measures such as completeness and correctness. This article also emphasises the types of roads which are giving better accuracy of extraction after fusion with hyperspectral image. This can vary because of types of material and condition of roads. The methodology was applied on roads of Dehradun, India.Keywords: hyperspectral image; image fusion: automatic road extraction IntroductionAttaining geospatial information is most important in spatial decision making and many applications in remote sensing and geographic information systems. When considering geospatial information for decision making for problems related to an urban environment, roads form an integral part of the data requirement. Rangwala (2000) described roads as a man-made linear feature, generally rectangular, which divides the town in more or less rectangular sectors. Detailed and accurate information about the road network is the foundation for comprehensive management and planning of transportation infrastructure and assets. Quality standards for the required data have evolved considerably over the last decades. Remote sensing has the potential to provide detailed road mapping and may offer more up-to-date and economical methods to improve road network updating. The geometry and structure of road surfaces result in a spectrally indistinct response with a high amount of spatial variability. These factors become even more complex and complicated within urban areas, where the road network is generally dense. The abundant presence of structures such as roofs, transportation surfaces, vegetation,

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Section: Literature Reviewmentioning

confidence: 99%

Exploiting IKONOS and Hyperion data fusion for automated road extraction

Tiwari

Pande

Aye³

2010

Geocarto International

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“…Since the laser operates in the near infrared, the resulting reflectance image can be used to discriminate between buildings and trees. Ford et al (1997) use hyperspectral data with nominal 2 meter ground sample distance and over 200 spectral samples per pixel. It is captured by the airborne sensor system HYDICE (hyperspectral digital imagery collection experiment).…”

Section: Detection Of Buildings By Classificationmentioning

confidence: 99%

3D Synthetic Environment Reconstruction

Abdelguerfi

2001

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All rights reserved. No part ofthis publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, mechanical, photo-copying, recording, or otherwise, without the prior written permission ofthe publisher, Springer Science+Business Media, LLC.Printed on acidfree paper. The Publisher offers discounts on this book for course use and bulk purchases. For further information, send email to . The cover art shows a 3D synthetic environment 0/ part 0/ the US Marine Corps' Military Operations in Urban Terrain /acility at Camp LeJeune, NC This synthetic environment was generated /rom a commercial-off-the-shelf database management system and VPF+. VPF+ adds 3D topology to the National Imagery and Mapping Agency's Vector Product Format standard and is the result 0/ research by the Naval Research Laboratory and the University 0/ New Orleans.

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