Destriping multisensor imagery with moment matching
Citations
287 citations
Cites background or methods from "Destriping multisensor imagery with..."
...In our experiment, we compare the destriping results of the proposed method with the moment-matching method [9], the total variation (TV)-based denoising method [39], and the K-singular value decomposition (Ksvd) method [40] on the four test subimages....
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...Moment-matching-based methods [9], [18] often assume that this relation can be described by a linear function....
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...Therein, moment matching [9], [18] and histogram matching [10], [19]– [21] are typical examples....
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...For the sake of comparison, the moment-matching method is implemented according to [9]....
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...2–5 show the destriping results of the four subimages with the moment-matching method [9], the TV-based denoising method [39], the Ksvd method [40], and the proposed method, respectively....
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254 citations
240 citations
Cites background or methods from "Destriping multisensor imagery with..."
...[9] use a moment matching method to remove the stripes in Landsat thematic mapper (TM) remote sensing images....
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...In this paper, the moment matching method is used [9]....
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...In this paper, it is assumed that the degradation process can be linearly described as in [9] and [17], but the existence of linear-assumption error is permitted....
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...Another destriping approach examines the distribution of digital numbers (DNs) for each sensor and adjusts this distribution to some reference distribution [7], [9]....
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208 citations
Cites methods from "Destriping multisensor imagery with..."
...A moment matching (MM) technique [56,57] has been adapted for HSI striping noise removal in [25] by exploiting the spectral correlations....
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187 citations
Cites background from "Destriping multisensor imagery with..."
...1(a), there exist eight stripe lines per ten lines, including six successive stripe lines with different variances....
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References
222 citations
"Destriping multisensor imagery with..." refers background or methods in this paper
...Horn and Woodham (1979) matched the histogram of each subscene to the histogram of the overall image. Brie y, a histogram of the relative frequency of DNs is generated for each sensor and for the overall image. Next, cumulative probability functions (CPFs) are generated from these histograms. Using the overall CPF (i.e. the CPF generated from the overall image) as a reference function, each sensor’s CPF is adjusted. For a given pixel, a cumulative probability value for its original DN is found from its sensor CPF, and that probability value on the reference CPF determines a corrected DN. In practice, the new value is not likely to be an integer and some rule needs to be adopted to select the lower or higher value of the range. This results in a slight (expected value 0.5 DN) change in the average image brightness, well within the noise levels of satellite imagery, but the bias it introduces may have implications in some applications, such as the estimation of atmospheric states using dark targets or in multispectral classi cation (since the bias will by chance be larger in some ranges of some sensor lines). We describe these errors as ‘binning errors’. Speci cally, the quantization of a continuous variable such as radiance into discrete values is analogous to sorting these values into bins, each bin representing a range of continuous values. KieŒer et al. (1985) showed that about 20% of Landsat-5 TM pixels are mis-binned to an adjacent level by the on-board analogue-to-digita l converter. The histogram equalization technique will introduce binning errors of 1 DN to half the pixels, on average, meaning that after histogram matching we could expect 10% of pixels to diŒer from their ‘correct’ value by 2 DN as a result of binning errors alone. Using the overall image as the reference CPF has the eŒect of increasing the spread of each sensor’s corrected values, since the overall image contains both withinsensor and between-sensor variance. If the striping eŒect is small in comparison to the scene variance, this may not result in visible eŒects, but it may exacerbate binning errors. Increasing the spread of a sensor histogram will result in some empty bins; a result that is unsatisfying when considering a response to a continuous variable like radiance. A particular sensor that diŒers systematically from the others, say in having a much larger oŒset or a larger tendency to fail and record a saturation value, has the potential to distort the overall histogram considerably. Using sensors with similar histograms to construct a reference histogram has been shown to improve destriping (Poros and Peterson 1985). However, matching to a particular ‘typical’ sensor is a simpler solution, and it entirely avoids contributions of sensorto-sensor variance. Algazi and Ford (1981) suggested using a reference sensor to destripe Landsat MSS imagery, but used an arbitrary sensor. To destripe GOES imagery, Weinreb et al. (1989) selected a reference sensor that was stable, low-noise, and did not show clipping at high or low DNs. Wegener (1990) noted histogram matching as proposed by Horn and Woodham (1979) can be sensitive to rather small violations of the similarity assumption. Typically, Landsat MSS band 7 shows a strong bimodality in scenes of mixed land and water. In a sample scene, Wegener (1990) showed that a diŒerence of less than 1% in the number of a single sensor’s pixels sampling water resulted in unsatisfactory destriping....
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...Horn and Woodham (1979) matched the histogram of each subscene to the histogram of the overall image. Brie y, a histogram of the relative frequency of DNs is generated for each sensor and for the overall image. Next, cumulative probability functions (CPFs) are generated from these histograms. Using the overall CPF (i.e. the CPF generated from the overall image) as a reference function, each sensor’s CPF is adjusted. For a given pixel, a cumulative probability value for its original DN is found from its sensor CPF, and that probability value on the reference CPF determines a corrected DN. In practice, the new value is not likely to be an integer and some rule needs to be adopted to select the lower or higher value of the range. This results in a slight (expected value 0.5 DN) change in the average image brightness, well within the noise levels of satellite imagery, but the bias it introduces may have implications in some applications, such as the estimation of atmospheric states using dark targets or in multispectral classi cation (since the bias will by chance be larger in some ranges of some sensor lines). We describe these errors as ‘binning errors’. Speci cally, the quantization of a continuous variable such as radiance into discrete values is analogous to sorting these values into bins, each bin representing a range of continuous values. KieŒer et al. (1985) showed that about 20% of Landsat-5 TM pixels are mis-binned to an adjacent level by the on-board analogue-to-digita l converter. The histogram equalization technique will introduce binning errors of 1 DN to half the pixels, on average, meaning that after histogram matching we could expect 10% of pixels to diŒer from their ‘correct’ value by 2 DN as a result of binning errors alone. Using the overall image as the reference CPF has the eŒect of increasing the spread of each sensor’s corrected values, since the overall image contains both withinsensor and between-sensor variance. If the striping eŒect is small in comparison to the scene variance, this may not result in visible eŒects, but it may exacerbate binning errors. Increasing the spread of a sensor histogram will result in some empty bins; a result that is unsatisfying when considering a response to a continuous variable like radiance. A particular sensor that diŒers systematically from the others, say in having a much larger oŒset or a larger tendency to fail and record a saturation value, has the potential to distort the overall histogram considerably. Using sensors with similar histograms to construct a reference histogram has been shown to improve destriping (Poros and Peterson 1985). However, matching to a particular ‘typical’ sensor is a simpler solution, and it entirely avoids contributions of sensorto-sensor variance. Algazi and Ford (1981) suggested using a reference sensor to destripe Landsat MSS imagery, but used an arbitrary sensor. To destripe GOES imagery, Weinreb et al. (1989) selected a reference sensor that was stable, low-noise, and did not show clipping at high or low DNs. Wegener (1990) noted histogram matching as proposed by Horn and Woodham (1979) can be sensitive to rather small violations of the similarity assumption....
[...]
...Horn and Woodham (1979) matched the histogram of each subscene to the histogram of the overall image. Brie y, a histogram of the relative frequency of DNs is generated for each sensor and for the overall image. Next, cumulative probability functions (CPFs) are generated from these histograms. Using the overall CPF (i.e. the CPF generated from the overall image) as a reference function, each sensor’s CPF is adjusted. For a given pixel, a cumulative probability value for its original DN is found from its sensor CPF, and that probability value on the reference CPF determines a corrected DN. In practice, the new value is not likely to be an integer and some rule needs to be adopted to select the lower or higher value of the range. This results in a slight (expected value 0.5 DN) change in the average image brightness, well within the noise levels of satellite imagery, but the bias it introduces may have implications in some applications, such as the estimation of atmospheric states using dark targets or in multispectral classi cation (since the bias will by chance be larger in some ranges of some sensor lines). We describe these errors as ‘binning errors’. Speci cally, the quantization of a continuous variable such as radiance into discrete values is analogous to sorting these values into bins, each bin representing a range of continuous values. KieŒer et al. (1985) showed that about 20% of Landsat-5 TM pixels are mis-binned to an adjacent level by the on-board analogue-to-digita l converter....
[...]
...Horn and Woodham (1979) matched the histogram of each subscene to the histogram of the overall image. Brie y, a histogram of the relative frequency of DNs is generated for each sensor and for the overall image. Next, cumulative probability functions (CPFs) are generated from these histograms. Using the overall CPF (i.e. the CPF generated from the overall image) as a reference function, each sensor’s CPF is adjusted. For a given pixel, a cumulative probability value for its original DN is found from its sensor CPF, and that probability value on the reference CPF determines a corrected DN. In practice, the new value is not likely to be an integer and some rule needs to be adopted to select the lower or higher value of the range. This results in a slight (expected value 0.5 DN) change in the average image brightness, well within the noise levels of satellite imagery, but the bias it introduces may have implications in some applications, such as the estimation of atmospheric states using dark targets or in multispectral classi cation (since the bias will by chance be larger in some ranges of some sensor lines). We describe these errors as ‘binning errors’. Speci cally, the quantization of a continuous variable such as radiance into discrete values is analogous to sorting these values into bins, each bin representing a range of continuous values. KieŒer et al. (1985) showed that about 20% of Landsat-5 TM pixels are mis-binned to an adjacent level by the on-board analogue-to-digita l converter. The histogram equalization technique will introduce binning errors of 1 DN to half the pixels, on average, meaning that after histogram matching we could expect 10% of pixels to diŒer from their ‘correct’ value by 2 DN as a result of binning errors alone. Using the overall image as the reference CPF has the eŒect of increasing the spread of each sensor’s corrected values, since the overall image contains both withinsensor and between-sensor variance. If the striping eŒect is small in comparison to the scene variance, this may not result in visible eŒects, but it may exacerbate binning errors. Increasing the spread of a sensor histogram will result in some empty bins; a result that is unsatisfying when considering a response to a continuous variable like radiance. A particular sensor that diŒers systematically from the others, say in having a much larger oŒset or a larger tendency to fail and record a saturation value, has the potential to distort the overall histogram considerably. Using sensors with similar histograms to construct a reference histogram has been shown to improve destriping (Poros and Peterson 1985). However, matching to a particular ‘typical’ sensor is a simpler solution, and it entirely avoids contributions of sensorto-sensor variance. Algazi and Ford (1981) suggested using a reference sensor to destripe Landsat MSS imagery, but used an arbitrary sensor....
[...]
...Horn and Woodham (1979) matched the histogram of each subscene to the histogram of the overall image....
[...]
139 citations
"Destriping multisensor imagery with..." refers background or methods in this paper
...To destripe GOES imagery, Weinreb et al. (1989) selected a reference sensor that was stable, low-noise, and did not show clipping at high or low DNs. Wegener (1990) noted histogram matching as proposed by Horn and Woodham (1979) can be sensitive to rather small violations of the similarity…...
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...To destripe GOES imagery, Weinreb et al. (1989) selected a reference sensor that was stable, low-noise, and did not show clipping at high or low DNs. Wegener (1990) noted histogram matching as proposed by Horn and Woodham (1979) can be sensitive to rather small violations of the similarity assumption....
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...Wegener (1990) found moment matching produced a noisy, banded image, but the commercial algorithm he used compared data only within a particular sensor sweep (i.e. within six lines in the MSS imagery used), which dramatically reduced the number of pixels per sensor....
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...In a sample scene, Wegener (1990) showed that a diŒerence of less than 1% in the number of a single sensor’s pixels sampling water resulted in unsatisfactory destriping....
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...coastline runs near-parallel to a scan line), the problem can be solved by using a larger image, or alternatively, by segmenting the image, in an extension of Wegener’s (1990) approach....
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134 citations
"Destriping multisensor imagery with..." refers background in this paper
...Since the variation is periodic, lters have been constructed which remove features at a given frequency (Srinivasan et al. 1988, Crippen 1989)....
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87 citations
"Destriping multisensor imagery with..." refers background in this paper
...To destripe GOES imagery, Weinreb et al. (1989) selected a reference sensor that was stable, low-noise, and did not show clipping at high or low DNs. Wegener (1990) noted histogram matching as proposed by Horn and Woodham (1979) can be sensitive to rather small violations of the similarity…...
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77 citations
"Destriping multisensor imagery with..." refers methods in this paper
...Fischel (1984) used moment matching in combination with calibration lamp data (not normally available to end-users) for Landsat TM optical bands and successfully removed all detector-to-detector striping (except binning errors) over homogeneous areas of water. An earlier implementation of moment matching by Rohde et al. (1978) included a logical error which biased the mean corrected value of sensors with high standard deviations upwards and produced unsatisfactory results. Care should be taken to use a fairly large image so that the assumption of statistically similar subscenes is likely to be met. Moment matching has been found to be unsatisfactory when test images are small. For example, Horn and Woodham (1979) used 364 by 430 pixels in a Landsat MSS image (6 sensors) and found moment matching to be unsatisfactory (some 26 000 pixels per sensor). Wegener (1990) found moment matching produced a noisy, banded image, but the commercial algorithm he used compared data only within a particular sensor sweep (i....
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...Fischel (1984) used moment matching in combination with calibration lamp data (not normally available to end-users) for Landsat TM optical bands and successfully removed all detector-to-detector striping (except binning errors) over homogeneous areas of water. An earlier implementation of moment matching by Rohde et al. (1978) included a logical error which biased the mean corrected value of sensors with high standard deviations upwards and produced unsatisfactory results....
[...]
...Fischel (1984) used moment matching in combination with calibration lamp data (not normally available to end-users) for Landsat TM optical bands and successfully removed all detector-to-detector striping (except binning errors) over homogeneous areas of water....
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...Fischel (1984) used moment matching in combination with calibration lamp data (not normally available to end-users) for Landsat TM optical bands and successfully removed all detector-to-detector striping (except binning errors) over homogeneous areas of water. An earlier implementation of moment matching by Rohde et al. (1978) included a logical error which biased the mean corrected value of sensors with high standard deviations upwards and produced unsatisfactory results. Care should be taken to use a fairly large image so that the assumption of statistically similar subscenes is likely to be met. Moment matching has been found to be unsatisfactory when test images are small. For example, Horn and Woodham (1979) used 364 by 430 pixels in a Landsat MSS image (6 sensors) and found moment matching to be unsatisfactory (some 26 000 pixels per sensor)....
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