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Quality assessment of CHRIS/PROBA image and recommendation for land cover classification

Quality assessment of CHRIS/PROBA image and recommendation for land cover classification
Quality assessment of CHRIS/PROBA image and recommendation for land cover classification
Quality assessment of satellite data forms an important part for any land use classification process and as a result every user should know the quality of the image used for preparation of a map. In the present day scenario, data from small experimental satellites are being used a lot for preparation of maps for land and water resources application. To assess the quality of these products, accuracy is in general the true value of the quantity that is being measured. An accuracy statement enables the end user to have a first hand knowledge about the cost involvement and the algorithms to be used for data calibration. The present study involved the i) quality assessment of nadir view CHRIS data (raw and destriped) and ii) recommendation for mapping land cover units. The images were processed and destriped using 1) DIELMO destriping method and 2) ESSC destriping algorithm using HDF Clean. The study looks into the procedure followed by the above two methods of destriping the raw data and their outcome. But the question that arises whether these destriping processes are at all effective in generation of landuse / landcover map? A comparative qualitative assessment of the raw image and the two images destriped by two different methods, were carried out. A quantitative analysis of the three images were also undertaken to find out whether the enhancement of the images by way of destriping helps in the classification process. The analysis was undertaken by i) statistical approach and ii) classification of the three images using unsupervised classification technique. A comparative study of the classified images generated from the raw data and data cleaned by ESSC algorithm and DIELMO was also carried out. This study on the basis of the quantitative and qualitative analysis recommends the best images for landcover classification.
chris/proba, image processing
118-126
Gupta, Niladri
8dec96a6-b85b-4202-a4ec-bb8f9ca030a3
Milton, E.J.
f6cb5c0d-a5d4-47d7-860f-096de08e0c24
Gupta, Niladri
8dec96a6-b85b-4202-a4ec-bb8f9ca030a3
Milton, E.J.
f6cb5c0d-a5d4-47d7-860f-096de08e0c24

Gupta, Niladri and Milton, E.J. (2009) Quality assessment of CHRIS/PROBA image and recommendation for land cover classification. Proceedings of the Remote Sensing and Photogrammetry Society Annual Conference 2009, United Kingdom. 08 - 11 Sep 2009. pp. 118-126 .

Record type: Conference or Workshop Item (Paper)

Abstract

Quality assessment of satellite data forms an important part for any land use classification process and as a result every user should know the quality of the image used for preparation of a map. In the present day scenario, data from small experimental satellites are being used a lot for preparation of maps for land and water resources application. To assess the quality of these products, accuracy is in general the true value of the quantity that is being measured. An accuracy statement enables the end user to have a first hand knowledge about the cost involvement and the algorithms to be used for data calibration. The present study involved the i) quality assessment of nadir view CHRIS data (raw and destriped) and ii) recommendation for mapping land cover units. The images were processed and destriped using 1) DIELMO destriping method and 2) ESSC destriping algorithm using HDF Clean. The study looks into the procedure followed by the above two methods of destriping the raw data and their outcome. But the question that arises whether these destriping processes are at all effective in generation of landuse / landcover map? A comparative qualitative assessment of the raw image and the two images destriped by two different methods, were carried out. A quantitative analysis of the three images were also undertaken to find out whether the enhancement of the images by way of destriping helps in the classification process. The analysis was undertaken by i) statistical approach and ii) classification of the three images using unsupervised classification technique. A comparative study of the classified images generated from the raw data and data cleaned by ESSC algorithm and DIELMO was also carried out. This study on the basis of the quantitative and qualitative analysis recommends the best images for landcover classification.

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More information

Published date: September 2009
Venue - Dates: Proceedings of the Remote Sensing and Photogrammetry Society Annual Conference 2009, United Kingdom, 2009-09-08 - 2009-09-11
Keywords: chris/proba, image processing

Identifiers

Local EPrints ID: 69863
URI: https://eprints.soton.ac.uk/id/eprint/69863
PURE UUID: 18bc3377-8bef-4fbd-a2a9-639f31020e4e

Catalogue record

Date deposited: 08 Dec 2009
Last modified: 19 Jul 2017 00:05

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