A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Chen, Xuexia; Vierling, Lee A.; Deering, Don

doi:10.1016/j.rse.2005.05.021

Cited by 240 publications

(154 citation statements)

References 31 publications

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“…The PIF method has been tested at several study sites [49][50][51][52][53], especially in water areas with haze contamination [45]. In the present study, the relatively haze-free HJ-1A satellite CCD2 image obtained on 28 August 2012, was used as a reference, and certain pixels were singled out as the pseudo-invariant feature set via the temporally invariant cluster (TIC) method [54]. The relationship built on the target image and base image obtained on 28 August 2012, was used to perform radiation standardization of the cloud-free HJ-1A/1B satellite CCD measurements from 24 October 2012, and for the six images from September to November 2008.…”

Section: The Pseudo-invariant Features Methodsmentioning

confidence: 99%

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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Using Deep Bay in China as an example, an effective method for the retrieval of total suspended sediment (TSS) concentration using HJ-1A/1B satellite images is proposed. The factors driving the variation of the TSS spatial distribution are also discussed. Two field surveys, conducted on August 29 and October 26, 2012, showed that there was a strong linear relationship (R 2 = 0.9623) between field-surveyed OBS (optical backscatter) measurements (5-31NTU) and laboratory-analyzed TSS concentrations (9.89-35.58 mg/L). The COST image-based atmospheric correction procedure and the pseudo-invariant features (PIF) method were combined to remove the atmospheric effects from the total radiance measurements obtained with different CCDs onboard the HJ-1A/1B satellites. Then, a simple and practical retrieval model was established based on the relationship between the satellite-corrected reflectance band ratio of band 3 and band 2 (Rrs3/Rrs2) and in-situ TSS measurements. The R 2 of the regression relationship was 0.807, and the mean relative error (MRE) was 12.78%, as determined through in-situ data validation. Finally, the influences of tide cycles, wind factors (direction and speed) and other factors on the OPEN ACCESSRemote Sens. 2014, 6 9912 variation of the TSS spatial pattern observed from HJ-1A/1B satellite images from September through November of 2008 are discussed. The results show that HJ-1A/1B satellite CCD images can be used to estimate TSS concentrations under different tides in the study area over synoptic scales without using simultaneous in-situ atmospheric parameters and spectrum data. These findings provide strong informational support for numerical simulation studies on the combined influence of tide cycles and other associated hydrologic elements in Deep Bay.

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Section: The Pseudo-invariant Features Methodsmentioning

confidence: 99%

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

et al. 2014

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“…In order to maintain the radiometric consistency of temporal remote sensing images radiometric corrections are carried out to account for errors caused by the variation in sensor characteristics, atmospheric condition, solar angle, and sensor view angle (Chen et al, 2005). Different radiometric correction methods are developed.…”

Section: Preparation Of Databasementioning

confidence: 99%

“…The relative radiometric correction /normalization (RRC) reduces atmospheric and other unexpected variation among multiple images by adjusting the radiometric properties of target images to match a base image (Janzen et al, 2006;Yuan and Elvidge, 1996). The RRC include methods such as dark object subtraction (Chavez, 1988), Pseudo Invariant Features (PIF) (Chen, et al, 2005), automatic scattergram controlled regression (ASCR) (Elvidge, et al,1995), Ridge method (Song, et al, 2001), and second simulation of the satellite signal in the solar spectrum (6S) (Vermote, et al,1997). We used dark object subtraction (DOS) method for radiometric normalization of bi-temporal LISS-III data.…”

Section: Preparation Of Databasementioning

confidence: 99%

Monitoring Urban Land-Cover Features using Resourcesat LISS-III Data

Prakash

Asra

Venkatesh

et al. 2015

IJARSG

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In urban environment especially in the trans-urban regions the changes in land-cover features are quite frequent and substantial. With availability of medium resolution satellite-data reliable and timely information on changes in land-cover features in the peripheral areas of the urban settlement could be generated. In a case study presented here, we have analyzed the land-cover changes in the trans-urban area of Hyderabad metropolitan city using bi-temporal Resourcesat -1 and-2 Linear Imaging self-scanning Sensor (LISS-III) data for the period 2005 and 2011. Radiometric normalization using dark object subtraction (DOS) method followed by co-registration of georeferenced datasets was carried out. Subsequently, image analysis for deriving land-cover features was carried out using Gaussian Maximum likelihood classifier (MLC). The results show that because of urbanization the most notable changes in spatial extent have occurred in the scrubs areas followed by cropland. The article describes in detail the changes that have taken place in the land cover features.

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“…From simple to sophisticated, various PIF-based methods were developed for image normalization, such as the Ridge method [2,[14][15][16], Simple image regression [4], Dark set-Bright set (DB) [17] and Automatic Scattergram Control Regression (ASAR) [18,19], etc. Specifically, Canty and Nielsen developed a powerful and widely used method, the iteratively reweighted multivariate alteration detection (iRMAD) transformation, which is invariant to linear and affine scaling.…”

Section: Introductionmentioning

confidence: 99%

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

Bai

Tang

2018

Remote Sensing

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Radiation normalization is an essential pre-processing step for generating high-quality satellite sequence images. However, most radiometric normalization methods are linear, and they cannot eliminate the regular nonlinear spectral differences. Here we introduce the well-established kernel canonical correlation analysis (kCCA) into radiometric normalization for the first time to overcome this problem, which leads to a new kernel method. It can maximally reduce the image differences among multi-temporal images regardless of the imaging conditions and the reflectivity difference. It also perfectly eliminates the impact of nonlinear changes caused by seasonal variation of natural objects. Comparisons with the multivariate alteration detection (CCA-based) normalization and the histogram matching, on Gaofen-1 (GF-1) data, indicate that the kCCA-based normalization can preserve more similarity and better correlation between an image-pair and effectively avoid the color error propagation. The proposed method not only builds the common scale or reference to make the radiometric consistency among GF-1 image sequences, but also highlights the interesting spectral changes while eliminates less interesting spectral changes. Our method enables the application of GF-1 data for change detection, land-use, land-cover change detection etc.

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A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Cited by 240 publications

References 31 publications

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

Monitoring Urban Land-Cover Features using Resourcesat LISS-III Data

kCCA Transformation-Based Radiometric Normalization of Multi-Temporal Satellite Images

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