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At least 19 records

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NASA NTRS1992

The impact of misregistration on change detection

The impact of images misregistration on the detection of changes in land cover was studied using spatially degraded Landsat MSS images. Emphasis is placed on simulated images of the Normalized Difference Vegetation Index (NDVI) at spatial resolutions of 250 and 500 m. It is pointed out that there is the need to achieve high values of registration accuracy. The evidence from simulations suggests that misregistrations can have a marked effect on the ability of remotely sensed data to detect changes in land cover. Even subpixel misregistrations can have a major impact, and the most marked proportional changes will tend to occur at the finest misregistrations.

Townshend, John R. G.↗

NASA NTRS2017

Band-to-Band Misregistration of the Images of MODIS Onboard Calibrators and Its Impact on Calibration

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard Terra and Aqua satellites are radiometrically calibrated on-orbit with a set of onboard calibrators (OBCs), including a solar diffuser, a blackbody, and a space view port through which the detectors can view the dark space. As a whisk-broom scanning spectroradiometer, thirty-six MODIS spectral bands are assembled in the along-scan direction on four focal plane assemblies (FPAs). These bands capture images of the same target sequentially with the motion of a scan mirror. Then the images are coregistered onboard by delaying the appropriate band-dependent amount of time, depending on the band locations on the FPA. While this coregistration mechanismis functioning well for the far-field remote targets such as earth view scenes or the moon, noticeable band-to-band misregistration in the along-scan direction has been observed for near field targets, particularly in OBCs. In this paper, the misregistration phenomenon is presented and analyzed. It is concluded that the root cause of the misregistration is that the rotating element of the instrument, the scan mirror, is displaced from the focus of the telescope primary mirror. The amount of the misregistrationis proportional to the band location on the FPA and is inversely proportional to the distance between the target and the scan mirror. The impact of this misregistration on the calibration of MODIS bands is discussed. In particular, the calculation of the detector gain coefficient m1of bands 8-16 (412 nm 870 nm) is improved by up to 1.5% for Aqua MODIS.

Wang, Zhipeng↗

NASA NTRS1976

Effects of misregistration on multispectral recognition

Spatial misregistration of multispectral scanner data occurs when two or more spectral band signals supposedly representing the same location are in fact data values generated from two or more overlapping or entirely different ground locations. A study was performed at the Environmental Research Institute of Michigan to determine what effect spatial misregistration may have on the accuracy of recognition processing of agriculturally oriented scanner data. It was found that misregistration severely reduces the availability of field center pixels and introduces significant errors in the classification accuracy and correct proportion estimation of a scene containing an inflated number of mixture pixels.

Cicone, R. C.↗

NASA NTRS1981

Modeling misregistration and related effects on multispectral classification

The effects of misregistration on the multispectral classification accuracy when the scene registration accuracy is relaxed from 0.3 to 0.5 pixel are investigated. Noise, class separability, spatial transient response, and field size are considered simultaneously with misregistration in their effects on accuracy. Any noise due to the scene, sensor, or to the analog/digital conversion, causes a finite fraction of the measurements to fall outside of the classification limits, even within nominally uniform fields. Misregistration causes field borders in a given band or set of bands to be closer than expected to a given pixel, causing additional pixels to be misclassified due to the mixture of materials in the pixel. Simplified first order models of the various effects are presented, and are used to estimate the performance to be expected.

Billingsley, F. C.↗

NASA NTRS1982

Modeling misregistration and related effects on multispectral classification

Misregistration is but one of a group of parameters (noise, class separability, spatial transient response, field sizes) affecting the accuracy of multispectral classification. The entire group must be considered simultaneously. Any noise in the measurements (due to the scene, to the sensor, or to the analog/digital conversion) will cause a finite fraction of the measurements to fall outside of the classification limits, even within nominally uniform fields. For field boundaries, where the effects of misregistration are felt, additional pixels will be misclassified due to the mixture of materials in the pixels. Misregistration causes field borders in a given (set of) band(s) to be closer than expected to a given pixel, causing additional pixels to be misclassified. Simplified models of the various effects are used to gain conceptual understanding and to estimate the performance to be expected.

Billingsley, F. C.↗

NASA NTRS1976

Investigation of spatial misregistration effects in multispectral scanner data

The author has identified the following significant results. A model for estimating the expected proportion of multiclass pixels in a scene was generalized and extended to include misregistration effects. Another substantial effort was the development of a simulation model to generate signatures to represent the distributions of signals from misregistered multiclass pixels, based on single class signatures. Spatial misregistration causes an increase in the proportion of multiclass pixels in a scene and a decorrelation between signals in misregistered data channels. The multiclass pixel proportion estimation model indicated that this proportion is strongly dependent on the pixel perimeter and on the ratio of the total perimeter of the fields in the scene to the area of the scene. Test results indicated that expected values computed with this model were similar to empirical measurements made of this proportion in four LACIE data segments.

Nalepka, R. F.↗

NASA NTRS1982

Modeling misregistration and related effects on multispectral classification

Any noise in measurements (due to the scene, sensor, or the analog to digital process) causes a finite fraction of measurements to fall outside of the classification limits. For field boundaries, where the misregistration effects are felt, the misregistration causes the border in a given (set of) band(s) to be closer than expected to a given pixel, so that the mixed materials in the pixels cause additional pixels to fall outside of the class limits. Considerations of the transient distance involved in the difference in brightness between adjacent fields, when scaled to "per pixel", allow the estimation of the width of the border zones. The entire problem is then scaled to field sizes to allow estimation of the global effects. This approach allows the estimation of the accuracy of multispectral classification which might be expected for field interiors, the useful number of quantization bits, and one set of criteria for an unbiased classifier.

Billingsley, F. C.↗

NASA NTRS1982

Misregistration's effects on classification and proportion estimation accuracy

The estimates of crop type and acreage are undertaken in the AgRISTARS program by registering multiple date acquisitions of small subareas of LANDSAT scenes (termed segments), and applying multispectral analysis to them. An important contribution to errors in classification and acreage estimates is misregistration between multiple acquisitions. The formula used to express this relationship is given and the operations applied are so shown in diagrams. The taking of a LANDSAT feature vector and the derivation of the brightness and greeness are illustrated. It is shown that for any given sensor IFOV geometry, typical populations of fields can be derived and histograms can be plotted of the number of fields against field size according to ground truth. As a function of the resolution element, the IFOV of the sensor can draw the proportion of pure pixels in a given crop. Because the thematic mapper has a smaller resolution, the proportion of pixels that are pure in any given area will be larger.

Juday, R. D.↗

NASA NTRS2016

A Note on the Temporary Misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

The Landsat-8 and Sentinel-2 sensors provide multi-spectral image data with similar spectral and spatial characteristics that together provide improved temporal coverage globally. Both systems are designed to register Level 1 products to a reference image framework, however, the Landsat-8 framework, based upon the Global Land Survey images, contains residual geolocation errors leading to an expected sensor-to-sensor misregistration of 38 m (2sigma). These misalignments vary geographically but should be stable for a given area. The Landsat framework will be readjusted for consistency with the Sentinel-2 Global Reference Image, with completion expected in 2018. In the interim, users can measure Landsat-to-Sentinel tie points to quantify the misalignment in their area of interest and if appropriate to reproject the data to better alignment.

Storey, James↗

NASA NTRS1975

Developing processing techniques for Skylab data

The author has identified the following significant results. The effects of misregistration and the scan-line-straightening algorithm on multispectral data were found to be: (1) there is greatly increased misregistration in scan-line-straightening data over conic data; (2) scanner caused misregistration between any pairs of channels may not be corrected for in scan-line-straightened data; and (3) this data will have few pure field center pixels than will conic data. A program SIMSIG was developed implementing the signature simulation model. Data processing stages of the experiment were carried out, and an analysis was made of the effects of spatial misregistration on field center classification accuracy. Fifteen signatures originally used for classifying the data were analyzed, showing the following breakdown: corn (4 signatures), trees (2), brush (1), grasses, weeds, etc. (5), bare soil (1), soybeans (1), and alfalfa (1).

Nalepka, R. F.↗

NASA NTRS1983

Thematic Mapper image quality: Preliminary results

Based on images analyzed so far, the band to band registration accuracy of the thematic mapper is very good. For bands within the same focal plane, the mean misregistrations are well within the specification, 0.2 pixels. For bands between the cooled and uncooled focal planes, there is a consistent mean misregistration of 0.5 pixels along-scan and 0.2-0.3 pixels across-scan. It exceeds the permitted 0.3 pixels for registration of bands between focal planes. If the mean misregistrations were removed by the data processing software, an analysis of the standard deviation of the misregistration indicates all band combinations would meet the registration specifications except for those including the thermal band. Analysis of the periodic noise in one image indicates a noise component in band 1 with a spatial frequency equivalent to 3.2 pixels in the along-scan direction.

Wrigley, R. C.↗

NASA NTRS1983

Assessment of Thematic Mapper band-to-band registration by the block correlation method

Rectangular blocks of pixels from one band image were statistically correlated against blocks centered on identical pixels from a second band image. The block pairs were shifted in pixel increments both vertically and horizontally with respect to each other and the correlation coefficient to the maximum correlation was taken as the best estimate of registration error for each block pair. For the band combinations of the Arkansas scene studied, the misregistration of TM spectral bands within the noncooled focal plane lie well within the 0.2 pixel target specification. Misregistration between the middle IR bands is well within this specification also. The thermal IR band has an apparent misregistration with TM band 7 of approximately 3 pixels in each direction. The TM band 3 has a misregistration of approximately 0.2 pixel in the across-scan direction and 0.5 pixel in the along-scan direction, with both TM bands 5 and 7.

Card, D. H.↗

NASA NTRS1985

Assessment of Thematic Mapper Band-to-band Registration by the Block Correlation Method

Card, D. H.↗

NASA NTRS1983

Thematic mapper image quality - Preliminary results

Wrigley, R. C.↗

NASA NTRS1975

S-192 analysis: Conventional and special data processing techniques

The author has identified the following significant results. Multispectral scanner data gathered over test sites in southeast Michigan were analyzed. This analysis showed the data to be somewhat deficient especially in terms of the limited signal range in most SDOs and also in regard to SDO-SDO misregistration. Further analysis showed that the scan line straightening algorithm increased the misregistration of the data. Data were processed using the conic format. The effects of such misregistration on classification accuracy was analyzed via simulation and found to be significant. Results of employing conventional as well as special, unresolved object, processing techniques were disappointing due, at least in part, to the limited signal range and noise content of the data. Application of a second class of special processing techniques, signature extension techniques, yielded better results. Two of the more basic signature extension techniques seemed to be useful in spite of the difficulties.

Nalepka, R. F.↗

NASA NTRS1984

Investigation of several aspects of LANDSAT 4/5 data quality

A second quadrant from the Sacramento, CA scene 44/33 acquired by LANDSAT-4 was tested for band to band resolution. Results show that all measured misregistrations are within 0.03 pixels for similar band pairs. Two LANDSAT-5 scenes (one from Corpus Christi, TX and the other from Huntsville, AL) were also tested for band to band resolution. All measured misregistrations in the Texas scene are less than 0.03 pixels. The across scan misregistration Alabama scene is -0.66 pixels and thus needs correction. A 512 x 512 pixel area of the Pacific Ocean was corrected for the pixel offsets. Modulation transfer function analysis of the San Mateo Bridge using data from the San Francisco scene was accomplished.

Wrigley, R. C.↗

NASA NTRS1975

Developing processing techniques for Skylab data

The author has identified the following significant results. An analysis of the conic data was completed for the spatial misregistration study. The effects of misregistration on classification and acreage estimation accuracy are being studied. Signatures were extracted for the primary ground covers in the test area.

Nalepka, R. F.↗

NASA NTRS1975

Studies of recognition with multitemporal remote sensor data

Characteristics of multitemporal data and their use in recognition processing were investigated. Principal emphasis was on satellite data collected by the LANDSAT multispectral scanner and on temporal changes throughout a growing season. The effects of spatial misregistration on recognition performance with multitemporal data were examined. A capability to compute probabilities of detection and false alarm was developed and used with simulated distributions for misregistered pixels. Wheat detection was found to be degraded and false alarms increased by misregistration effects. Multitemporal signature characteristics and multitemporal recognition processing were studied to gain insights into problems associated with this approach and possible improvements. Recognition performance with one multitemporal data set displayed marked improvements over results from single-time data.

Malila, W. A.↗