Destriping Technique Research Articles

Enhancement of Image Classification for Forest Encroachment Mapping With Destriped SWIR Band in the Wavelet Domain

Commonly used destriping techniques in remote sensing applications have underlying Fourier transform process which tends to alter heavily the digital number values of surface features; thus, impacting its subsequent use in any image classification scheme. In this paper, we develop a novel destriping technique in the wavelet domain where statistical measures namely, standard deviation, signal-to-noise ratio, and entropy of the image and stripes are integrated in the wavelet filter design. Wavelet transform essentially keeps spatial information of the stripes in the transformed domain; thus, allowing a wavelet to filter stripes at different scales with minimal effect on image pixels relating to the surface features. We further propose a novel s for an environmental application, where a destriped band in the wavelet domain is included in image classification for forest encroachment mapping. The technique is implemented on IRS-1D LISS-III data for the mapping of forest encroachment in the Andaman and Nicobar Island, India, wherein the short wave infrared (SWIR) band is destriped and included in the image classification based on the linear support vector machine method. Statistical accuracy in terms of Kappa values for the resultant classified images by 1) including striped SWIR, 2) excluding SWIR, and 3) using destriped SWIR band are 0.97, 0.98, and 1, respectively. Z -tests indicate that the thematic map with destriped SWIR band is statistically significantly better than the other two maps.

Planck2015 results

This paper describes the mapmaking procedure applied to Planck Low Frequency Instrument (LFI) data. The mapmaking step takes as input the calibrated timelines and pointing information. The main products are sky maps of I, Q, and U Stokes components. For the first time, we present polarization maps at LFI frequencies. The mapmaking algorithm is based on a destriping technique, which is enhanced with a noise prior. The Galactic region is masked to reduce errors arising from bandpass mismatch and high signal gradients. We apply horn-uniform radiometer weights to reduce the effects of beam-shape mismatch. The algorithm is the same as used for the 2013 release, apart from small changes in parameter settings. We validate the procedure through simulations. Special emphasis is put on the control of systematics, which is particularly important for accurate polarization analysis. We also produce low-resolution versions of the maps and corresponding noise covariance matrices. These serve as input in later analysis steps and parameter estimation. The noise covariance matrices are validated through noise Monte Carlo simulations. The residual noise in the map products is characterized through analysis of half-ring maps, noise covariance matrices, and simulations.

VIIRS Day/Night Band—Correcting Striping and Nonuniformity over a Very Large Dynamic Range

The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities—day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time.

Iterative destriping and photometric calibration forPlanck-HFI, polarized, multi-detector map-making

We present an iterative scheme designed to recover calibrated I, Q, and U maps from Planck-HFI data using the orbital dipole due to the satellite motion with respect to the Solar System frame. It combines a map reconstruction, based on a destriping technique, juxtaposed with an absolute calibration algorithm. We evaluate systematic and statistical uncertainties incurred during both these steps with the help of realistic, Planck-like simulations containing CMB, foreground components and instrumental noise, and assess the accuracy of the sky map reconstruction by considering the maps of the residuals and their spectra. In particular, we discuss destriping residuals for polarization sensitive detectors similar to those of Planck-HFI under different noise hypotheses and show that these residuals are negligible (for intensity maps) or smaller than the white noise level (for Q and U Stokes maps), for l > 50. We also demonstrate that the combined level of residuals of this scheme remains comparable to those of the destriping-only case except at very low l where residuals from the calibration appear. For all the considered noise hypotheses, the relative calibration precision is on the order of a few 10e-4, with a systematic bias of the same order of magnitude.

Making cosmic microwave background temperature and polarization maps with MADAM

Madam is a CMB map-making code, designed to make temperature and polarization maps of time-ordered data of total power experiments like Planck. The algorithm is based on the destriping technique, but it also makes use of known noise properties in the form of a noise prior. The method in its early form was presented in an earlier work by Keihanen et al. (2005). In this paper we present an update of the method, extended to non-averaged data, and include polarization. In this method the baseline length is a freely adjustable parameter, and destriping can be performed at a different map resolution than that of the final maps. We show results obtained with simulated data. This study is related to Planck LFI activities.

Automatic destriping of Hyperion imagery based on spectral moment matching

As a pushbroom imaging spectrometer, the Hyperion sensor records each along-track image column with a single detector element in one of its area detector arrays. This image acquisition configuration is prone to exhibiting along-track striping artefacts, if the instrument has not undergone a recent, proper uniformity calibration. These defective columns must be corrected before performing further processing and quantitative analyses. Spatial moment matching (SpaMM) is currently the most commonly used method for destriping Hyperion imagery. However, since SpaMM was originally developed for dealing with remotely sensed multispectral data, the abundant spectral information embedded in hyperspectral data is not fully utilized in the method. This paper proposes a new methodology that can automatically and more accurately remove the striping artefacts from Hyperion imagery. The technique is called spectral moment matching (SpcMM) because it uses spectral autocorrelation instead of spatial autocorrelation to estimate the expected mean and standard deviation of a subscene, which is comprised of the image pixels acquired by an identical detector element (an along-track image column in the case of Hyperion data). The basis of the algorithm is the observation that there are usually highly correlated groups of bands in a hyperspectral image cube; the statistics of the subscenes measured by the corresponding detectors in a set of highly correlated bands are usually very similar. The possibility of introducing undesired side effects into the destriped images by the proposed SpcMM is minimized due to the proper estimation of the expected mean and standard deviation for each along-track column. Moreover, SpcMM can automatically destripe an entire Hyperion image cube without the manual selection of defective bands or across-track spatial regions, or the individual selection of band-specific window sizes for spatial smoothing. Two Hyperion datasets acquired over sites in Seal Harbour, Canada, and Coleambally, Australia, have been used to assess the proposed new destriping technique. Examination of the images destriped by SpcMM and SpaMM and the reflectance spectra retrieved from the destriped image cubes reveals that the presented algorithm removes various types of stripes without degrading the images and is superior to the spatial moment matching technique.

Madam- a map-making method for CMB experiments

We present a new map-making method for cosmic microwave background (CMB) measurements. The method is based on the destriping technique, but it also utilizes information about the noise spectrum. The low-frequency component of the instrument noise stream is modelled as a superposition of a set of simple base functions, whose amplitudes are determined by means of maximum-likelihood analysis, involving the covariance matrix of the amplitudes. We present simulation results with 1/f noise and show a reduction in the residual noise with respect to ordinary destriping. This study is related to Planck Low Frequency Instrument (LFI) activities.

A maximum likelihood approach to the destriping technique

The destriping technique is a viable tool for removing different kinds of systematic effects in CMB related experiments. It has already been proven to work for gain instabilities that produce the so-called 1/f noise and periodic fluctuations due to e.g. thermal instability. Both effects when coupled with the observing strategy result in stripes on the observed sky region. Here we present a maximum-likelihood approach to this type of technique and provide also a useful generalization. As a working case we consider a data set similar to what the Planck satellite will produce in its Low Frequency Instrument (LFI). We compare our method to those presented in the literature and find some improvement in performance. Our approach is also more general and allows for different base functions to be used when fitting the systematic effect under consideration. We study the effect of increasing the number of these base functions on the quality of signal cleaning and reconstruction. This study is related to Planck LFI activities.

Cosmic microwave background power spectrum estimation with the destriping technique

Extraction of the cosmic microwave background (CMB) angular power spectrum is a challenging task for current and future CMB experiments due to the large data sets involved. Here we describe an implementation of Monte Carlo apodized spherical transform estimator (MASTER) described in Hivon et al., which exploits the destriping technique as a map-making method. In this method a noise estimate based on destriped noise-only Monte Carlo (MC) simulations is subtracted from the pseudo-angular power spectrum. As a working case we use realistic simulations of the Planck low-frequency instrument (LFI). We found that the effect of destriping on a pure sky signal is minimal and requires no correction. Instead we found an effect related to the distribution of detector pointings, which affects the high-ℓ part of the power spectrum. We correct for this by subtracting a ‘signal bias’ estimated by MC simulations. We also give analytical estimates for this signal bias. Our method is fast and accurate enough (the estimator is unbiased and errors are close to theoretical expectations for maximal accuracy) to estimate the CMB angular power spectra for current and future CMB space missions. This study is related to Planck LFI activities.

Removing $1/f$ noise stripes in cosmic microwave background anisotropy observations

Removal of systematic effects is crucial in present and future CMB experiments mapping large fraction of the sky. Accurate CMB measurements ask for multi-feed array instruments observing the sky with a redundant scanning strategy covering the same sky region on different time scales and with different detectors for a better control of systematic effects. We investigate the capability to suppress noise features in Time Ordered Data (TOD) by using the destriping technique described in Maino et al. (1999), under realistic assumptions for crossing condition between different scan circles and sky signal fluctuations on small angular scales. We perform, as a working case, Planck-LFI simulated observations with few arcminutes pixel size convolved with LFI beam resolutions. In the noiseless case for crossing condition based on pixels with side larger than the input one, the destriping algorithm inserts extra-noise in the final map of the order of K in rms and few μK in peak-to-peak amplitude at 30 GHz. However including instrumental noise (white and noise) in the TOD, the impact of the sky signal on the destriping is found to be very small. In addition, for crossing condition based on pixels with side half of the one of the final map (typically ~1/3 of the FWHM), we find only a small improvement (∼1% level) in the destriping quality with respect to the case when crossings are searched on pixels with same size of the final map one. We can conclude that the receiver noise is the driver for destriping quality. We extend the analysis to high values of the knee frequency and find that, although significantly suppressed by destriping, the residual additional noise rms is ∼31% larger than the pure white noise rms at Hz which could be a critical issue in the extraction of CMB angular power spectrum. We verified that the approximation of the noise on averaged scan circles as a single baseline still works well even for these high values of the knee frequency. Furthermore, by comparing simulations with different noise levels and different sampling rates, we find that the destriping quality does not significantly depend on the receiver sensitivity whereas it improves proportionally to the improvement of sampling rate. Therefore given a noise level, the higher the sampling rate, the better the destriping quality. This paper is based upon Planck-LFI activities.

Wavelet analysis for the elimination of striping noise in satellite images

A new destriping technique based on wavelet analysis and its application to satellite imagery is presented. The method was tested on a heavily striped Landsat MSS image using Daubechies wavelets of different orders. Qualitative and quantitative analyses were carried out to evaluate the performance for each wavelet, both by visual inspection and by measuring the signal-to-noise ratio of the denoised images. The outcome demonstrates the viability of the wavelet transform as a destriping tool by significantly reducing striping noise. Furthermore, the method proved to defeat some of the problems commonly found with traditional destriping techniques, such as the Gibbs phenomenon, border effects, edge blurring, and the preservation of radiometric level. In addition to its relatively easy implementation, the method is inexpensive in computer time and storage space, as well as suitable for application to problems other than the field of remote sensing.