Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy

R. M. Narayanan, M. K. Desetty, S. E. Reichenbach

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

The information content of remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and also the final application for which the imagery has been acquired. Various textural measures are used to characterize the image information content, based upon which different image processing algorithms are employed to enhance this quantity. Previous work in this area has resulted in three different approaches for quantifying image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. Our approach to quantifying image information content is based upon classification accuracy. We propose an exponential model for information content based upon target-background contrast, and target size relative to pixel size. The model is seen to be applicable for relating information content to spatial resolution for real Landsat Thematic Mapper (TM) as well as Shuttle Imaging Radar-C (SIR-C) images. An interesting conclusion that emerges from this model is that although the TM image has higher information content than the SIR-C image at smaller pixel sizes, the opposite is true at larger pixel sizes. The transition occurs at a pixel size of about 720 m. This tells us that for applications that require high resolution (or smaller pixel sizes), the TM sensor is more useful for terrain classification, while for applications involving lower resolutions (or larger pixel sizes), the SIR-C sensor has an advantage. Thus, the model is useful in comparing different sensor types for different applications.

Original languageEnglish (US)
Pages (from-to)537-553
Number of pages17
JournalInternational Journal of Remote Sensing
Volume23
Issue number3
DOIs
StatePublished - Feb 10 2002

Fingerprint

Remote sensing
spatial resolution
imagery
Pixels
remote sensing
pixel
Radar imaging
Sensors
radar
sensor
effect
Image processing
Entropy
image processing
Landsat thematic mapper
entropy

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy. / Narayanan, R. M.; Desetty, M. K.; Reichenbach, S. E.

In: International Journal of Remote Sensing, Vol. 23, No. 3, 10.02.2002, p. 537-553.

Research output: Contribution to journalArticle

@article{8347fe7e8fe649528473419d35331773,
title = "Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy",
abstract = "The information content of remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and also the final application for which the imagery has been acquired. Various textural measures are used to characterize the image information content, based upon which different image processing algorithms are employed to enhance this quantity. Previous work in this area has resulted in three different approaches for quantifying image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. Our approach to quantifying image information content is based upon classification accuracy. We propose an exponential model for information content based upon target-background contrast, and target size relative to pixel size. The model is seen to be applicable for relating information content to spatial resolution for real Landsat Thematic Mapper (TM) as well as Shuttle Imaging Radar-C (SIR-C) images. An interesting conclusion that emerges from this model is that although the TM image has higher information content than the SIR-C image at smaller pixel sizes, the opposite is true at larger pixel sizes. The transition occurs at a pixel size of about 720 m. This tells us that for applications that require high resolution (or smaller pixel sizes), the TM sensor is more useful for terrain classification, while for applications involving lower resolutions (or larger pixel sizes), the SIR-C sensor has an advantage. Thus, the model is useful in comparing different sensor types for different applications.",
author = "Narayanan, {R. M.} and Desetty, {M. K.} and Reichenbach, {S. E.}",
year = "2002",
month = "2",
day = "10",
doi = "10.1080/01431160010025970",
language = "English (US)",
volume = "23",
pages = "537--553",
journal = "International Joural of Remote Sensing",
issn = "0143-1161",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

TY - JOUR

T1 - Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy

AU - Narayanan, R. M.

AU - Desetty, M. K.

AU - Reichenbach, S. E.

PY - 2002/2/10

Y1 - 2002/2/10

N2 - The information content of remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and also the final application for which the imagery has been acquired. Various textural measures are used to characterize the image information content, based upon which different image processing algorithms are employed to enhance this quantity. Previous work in this area has resulted in three different approaches for quantifying image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. Our approach to quantifying image information content is based upon classification accuracy. We propose an exponential model for information content based upon target-background contrast, and target size relative to pixel size. The model is seen to be applicable for relating information content to spatial resolution for real Landsat Thematic Mapper (TM) as well as Shuttle Imaging Radar-C (SIR-C) images. An interesting conclusion that emerges from this model is that although the TM image has higher information content than the SIR-C image at smaller pixel sizes, the opposite is true at larger pixel sizes. The transition occurs at a pixel size of about 720 m. This tells us that for applications that require high resolution (or smaller pixel sizes), the TM sensor is more useful for terrain classification, while for applications involving lower resolutions (or larger pixel sizes), the SIR-C sensor has an advantage. Thus, the model is useful in comparing different sensor types for different applications.

AB - The information content of remote sensing imagery depends upon various factors such as spatial and radiometric resolutions, spatial scale of the features to be imaged, radiometric contrast between different target types, and also the final application for which the imagery has been acquired. Various textural measures are used to characterize the image information content, based upon which different image processing algorithms are employed to enhance this quantity. Previous work in this area has resulted in three different approaches for quantifying image information content, primarily based on interpretability, mutual information, and entropy. These approaches, although well refined, are difficult to apply to all types of remote sensing imagery. Our approach to quantifying image information content is based upon classification accuracy. We propose an exponential model for information content based upon target-background contrast, and target size relative to pixel size. The model is seen to be applicable for relating information content to spatial resolution for real Landsat Thematic Mapper (TM) as well as Shuttle Imaging Radar-C (SIR-C) images. An interesting conclusion that emerges from this model is that although the TM image has higher information content than the SIR-C image at smaller pixel sizes, the opposite is true at larger pixel sizes. The transition occurs at a pixel size of about 720 m. This tells us that for applications that require high resolution (or smaller pixel sizes), the TM sensor is more useful for terrain classification, while for applications involving lower resolutions (or larger pixel sizes), the SIR-C sensor has an advantage. Thus, the model is useful in comparing different sensor types for different applications.

UR - http://www.scopus.com/inward/record.url?scp=0037050759&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037050759&partnerID=8YFLogxK

U2 - 10.1080/01431160010025970

DO - 10.1080/01431160010025970

M3 - Article

AN - SCOPUS:0037050759

VL - 23

SP - 537

EP - 553

JO - International Joural of Remote Sensing

JF - International Joural of Remote Sensing

SN - 0143-1161

IS - 3

ER -