Imaging sensors for remote sensing

Joseph, George

doi:10.1080/02757259609532307

Cited by 17 publications

(9 citation statements)

References 36 publications

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“…Dwell time is relatively small in this imaging mode. Typical sensors utilizing this mode of imaging include LANDSAT Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) [5,6].…”

Section: -Integration Time and Imaging Modesmentioning

confidence: 99%

“…iii) Time-delay-and integration (TDI): In this mode, Fig.1c, multi-linear arrays are used instead of a single linear array used in the normal pushbroom mode, for more increasing of the dwell time. IKONOS-1 satellite sensor is a typical example using the TDI imaging mode [5,6].…”

Section: -Integration Time and Imaging Modesmentioning

confidence: 99%

“…Some examples of opto-mechanical, normal pushbroom, and TDI pushbroom scanners of similar detector types and scan characteristics are selected for analysis and trade-off study of S/N dependent scanners design parameters. [3,6]. The design parameters of these scanners are given in Table 2.…”

Section: -Parameters Of Selected Scannersmentioning

confidence: 99%

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Analysis and Trade-Off Study of Signal-to-Noise Ratio Dependent Design Parameters of Satellite Passive Electro-Optical Remote Sensors

Hasan

Mahmoud²,

Dayem

et al. 2007

International Conference on Aerospace Sciences and Aviation Tec

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One of the main goals that is concerned to remote sensor designers is the quality of the produced image. There are a number of parameters to evaluate the quality of the image produced by an imaging sensor. The parameters widely used are the Modulation Transfer Function (MTF) and signal-to-noise ratio (S/N), for passive electro-optical remote sensors. In this paper an analysis and trade-off study of S/N dependent sensor design parameters are presented.

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Section: -Integration Time and Imaging Modesmentioning

confidence: 99%

Section: -Integration Time and Imaging Modesmentioning

confidence: 99%

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Analysis and Trade-Off Study of Signal-to-Noise Ratio Dependent Design Parameters of Satellite Passive Electro-Optical Remote Sensors

Hasan

Mahmoud²,

Dayem

et al. 2007

International Conference on Aerospace Sciences and Aviation Tec

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“…IRS 1C/1D WiFS is a large-swath, high-repetivity camera designed specifically for vegetation and agricultural monitoring applications (Joseph, 1996;Pandya et al, 2000). Demonstrated approaches with WiFS are forest cover assessment, vegetation and crop phenology studies, wheat crop acreage estimation, land use/land cover mapping, drought crop discrimination and inventory, etc.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of approaches for AWiFS multi-date registration

Garg

Agrawal

Dadhwal

2008

International Journal of Applied Earth Observation and Geoinfor

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“…The earlier satellites, 1A, 1B and P2 had Linear Imaging Self Scanner (LISS) cameras called LISS-I and LISS-II which have been replaced by LISS-III and Wide Field (WiFS) sensors on 1C, 1D and P3 series of satellites. These cameras have been described by Joseph (1996). The inherent potential of the IRS series of satellite lies in the fact that it is equipped with high spectral and radiometric resolution ( IRS-P3: MOS, IRS-P4: OCM) sensors along with high temporal resolution with large swath WiFS and high spatial resolution LISS-III and PAN sensors.…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

Singh

Sridhar

Dadhwal

et al. 2002

Geocarto International

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Digital data from four multi spectral sensors having different spatial resolution and spectral channel, LISS-III, LISS-II, LISS-I and WiFS acquired from two Indian Remote Sensing satellite platform ( IRS-1B, IRS-IC) were evaluated for wheat crop classification accuracy over central India. Classification accuracy was assessed at different spatial resolutions using common red and near infrared (nir) bands in the above four sensors data as well as simulated intermediate resolution data. It was observed that accuracy improved with better spatial resolution and there was a considerable increases in accuracy from 188 to 144 m resolution and from 72 m up to 23 m resolution with the modest increase in the intermediate range. Inclusion of the MIR band, and choice of proper acquisition date resulted in significantly higher crop classification accuracies. The results have been evaluated against the future sensor planned on IRS Platform.

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Imaging sensors for remote sensing

Cited by 17 publications

References 36 publications

Analysis and Trade-Off Study of Signal-to-Noise Ratio Dependent Design Parameters of Satellite Passive Electro-Optical Remote Sensors

Analysis and Trade-Off Study of Signal-to-Noise Ratio Dependent Design Parameters of Satellite Passive Electro-Optical Remote Sensors

Evaluation of approaches for AWiFS multi-date registration

Comparative Evaluation of Indian Remote Sensing Multi‐Spectral Sensors Data for Crop Classification

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