Spectral misregistration correction and simulation for hyperspectral imagery

Radiometrically calibrated radiance hyperspectral images can be converted into reflectance images using atmospheric correction in order to extract useful ground information. There are some artifacts in the converted reflectance images due to spectrally misregistered sensor and atmospheric model error. These artifacts give coherent saw-tooth effects in the spectra of the reflectance imagery. These effects degrade the performance of classification and target detection algorithms and make them difficult to compare with ground target spectra. Three spectral misregistration compensation methods were developed in order to compensate for the consistent noise effects. If a ground truth spectrum exists for a test image, the ground truth spectrum can be divided by the spectrum derived from the reflectance image. This will give a coefficient indicating the difference between the ground truth spectrum and the noisy spectrum in the reflectance image. Multiplying this coefficient spectrum and the reflectance image spectrum can correct the saw-tooth effects. The other methods use the Cubic Spline smoothing technique. Cubic Spline smoothing is a fitting algorithm with a non-local smoothing method. Cubic spline smoothing can smooth out the saw-tooth noise in the spectra then the correction coefficient can be calculated as describe above. It is important to find relatively pure and unmixed pixels for the correction coefficient. Two methods for identifying relatively pure pixels were used for this research. The first is the Uniform Region method that is to identify the pixels with small standard deviation values among neighbor pixels. The second method is the Least Ratio method that is used to calculate ratios (standard deviation between smoothed and non-smoothed spectra divided by average reflectance of the spectra) and then calculate the correction coefficient using pixels having small ratios. Spectral misregistration was also simulated using MODTRAN lookup table and DIRSIG (The Digital Imaging and Remote Sensing Image Generation) synthetic image to understand and characterize the effect of spectral misregistration. The spectral misregistration compensation algorithms were tested and verified by the performance measurement of classification and target detection algorithms for test images (real and synthetic images)

Single pixel target detection using multispectral background changes

Possible methods to help a remote sensing analyst to find a static or moving single pixel target over vast areas of terrain were examined in this work. Specifically, the research deals with the particular problem of how to find these targets using multiple images of the same area that were collected with the same multispectral (6 bands) imaging sensor but with a background that changes between images. For this, hyperspectral quadratic covariance-based anomalous change detection algorithms were investigated to see if they could be used with multispectral data to find a moving target. In addition, a new method based on change vector analysis was developed to find a static target. In the case of the moving target problem, the performance of the Chronochrome, Covariance Equalization, and the Hyperbolic anomalous change detection algorithms were compared relative to each other and to a straight target detection algorithm. In addition, modifications to the covariance-based algorithms were developed that improved the results. For the static target case, various multispectral images were layer stacked together. Then, the Spectral Matched Filter hyperspectral target detection algorithm was applied on these data cubes to explore if this method could help separate a real target from false alarms obtained when simply running a target detection algorithm on a multispectral data cube. The analysis demonstrated that a significant reduction in the number of false alarms can be obtained with these methods when compared to traditional Spectral Matched Filter (SMF) algorithm to find either static or dynamic single pixel targets of interest. In addition, the analysis shows the limitations and behavior of these methods under some of the issues normally encountered in remote sensing imaging. Overall, it was demonstrated that periodic multispectral imagery collections over a wide area can be very useful to find targets of interest

Research Issues in Image Registration for Remote Sensing

Image registration is an important element in data processing for remote sensing with many applications and a wide range of solutions. Despite considerable investigation the field has not settled on a definitive solution for most applications and a number of questions remain open. This article looks at selected research issues by surveying the experience of operational satellite teams, application-specific requirements for Earth science, and our experiments in the evaluation of image registration algorithms with emphasis on the comparison of algorithms for subpixel accuracy. We conclude that remote sensing applications put particular demands on image registration algorithms to take into account domain-specific knowledge of geometric transformations and image content

A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data

Line scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a scene. These data can be useful for detecting sub-pixel targets. Such systems, however, may be limited by certain key characteristics in their design. Systems employing multiple spectrometers, or that collect data from multiple focal planes may suffer an inherent misregistration between sets of collected spectral bands. In order to utilize the full spectrum for target detection purposes, the sets of bands must be registered to each other as precisely as possible. Perfect registration is not possible, due to both the sensor design, and variation in sensor orientation during data acquisition. The issue can cause degradation in the performance of various target detection algorithms. An analysis of algorithms is necessary to determine which perform well when working with misregistered data. In addition, new algorithms may need to be developed which are more robust in these conditions. The work set forth in this thesis will improve the registration between spectral bands in a line scanning hyperspectral sensor by using a geometric model of the sensor along with aircraft orientation parameters to pair sets of image pixels based on their ground locations. Synthetic scenes were created and band-to-band misregistration was induced between the VIS and NIR spectral channels to test the performance of various hyperspectral target detection algorithms when applied to misregistered hyperspectral data. The results for this case studied show geometric algorithms perform well using only the VIS portion of the EM spectrum, and do not always benefit from the addition of NIR bands, even for small amounts of misregistration. Stochastic algorithms appear to be more robust than geometric algorithms for datasets with band-to-band misregistration. The stochastic algorithms tested often benefit from the addition of NIR bands, even for large amounts of misregistration

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

Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

LANDSAT-4/5 image data quality analysis

A LANDSAT Thematic Mapper (TM) quality evaluation study was conducted to identify geometric and radiometric sensor errors in the post-launch environment. The study began with the launch of LANDSAT-4. Several error conditions were found, including band-to-band misregistration and detector-to detector radiometric calibration errors. Similar analysis was made for the LANDSAT-5 Thematic Mapper and compared with results for LANDSAT-4. Remaining band-to-band misregistration was found to be within tolerances and detector-to-detector calibration errors were not severe. More coherent noise signals were observed in TM-5 than in TM-4, although the amplitude was generally less. The scan direction differences observed in TM-4 were still evident in TM-5. The largest effect was in Band 4 where nearly a one digital count difference was observed. Resolution estimation was carried out using roads in TM-5 for the primary focal plane bands rather than field edges as in TM-4. Estimates using roads gave better resolution. Thermal IR band calibration studies were conducted and new nonlinear calibration procedures were defined for TM-5. The overall conclusion is that there are no first order errors in TM-5 and any remaining problems are second or third order

Automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images

Die in den letzten Jahrzehnten aufgenommenen Satellitenbilder zur Erdbeobachtung bieten eine ideale Grundlage für eine genaue Langzeitüberwachung und Kartierung der Erdoberfläche und Atmosphäre. Unterschiedliche Sensoreigenschaften verhindern jedoch oft eine synergetische Nutzung. Daher besteht ein dringender Bedarf heterogene Multisensordaten zu kombinieren und als geometrisch und spektral harmonisierte Zeitreihen nutzbar zu machen. Diese Dissertation liefert einen vorwiegend methodischen Beitrag und stellt zwei neu entwickelte Open-Source-Algorithmen zur Sensorfusion vor, die gründlich evaluiert, getestet und validiert werden. AROSICS, ein neuer Algorithmus zur Co-Registrierung und geometrischen Harmonisierung von Multisensor-Daten, ermöglicht eine robuste und automatische Erkennung und Korrektur von Lageverschiebungen und richtet die Daten an einem gemeinsamen Koordinatengitter aus. Der zweite Algorithmus, SpecHomo, wurde entwickelt, um unterschiedliche spektrale Sensorcharakteristika zu vereinheitlichen. Auf Basis von materialspezifischen Regressoren für verschiedene Landbedeckungsklassen ermöglicht er nicht nur höhere Transformationsgenauigkeiten, sondern auch die Abschätzung einseitig fehlender Spektralbänder. Darauf aufbauend wurde in einer dritten Studie untersucht, inwieweit sich die Abschätzung von Brandschäden aus Landsat mittels synthetischer Red-Edge-Bänder und der Verwendung dichter Zeitreihen, ermöglicht durch Sensorfusion, verbessern lässt. Die Ergebnisse zeigen die Effektivität der entwickelten Algorithmen zur Verringerung von Inkonsistenzen bei Multisensor- und Multitemporaldaten sowie den Mehrwert einer geometrischen und spektralen Harmonisierung für nachfolgende Produkte. Synthetische Red-Edge-Bänder erwiesen sich als wertvoll bei der Abschätzung vegetationsbezogener Parameter wie z. B. Brandschweregraden. Zudem zeigt die Arbeit das große Potenzial zur genaueren Überwachung und Kartierung von sich schnell entwickelnden Umweltprozessen, das sich aus einer Sensorfusion ergibt.Earth observation satellite data acquired in recent years and decades provide an ideal data basis for accurate long-term monitoring and mapping of the Earth's surface and atmosphere. However, the vast diversity of different sensor characteristics often prevents synergetic use. Hence, there is an urgent need to combine heterogeneous multi-sensor data to generate geometrically and spectrally harmonized time series of analysis-ready satellite data. This dissertation provides a mainly methodical contribution by presenting two newly developed, open-source algorithms for sensor fusion, which are both thoroughly evaluated as well as tested and validated in practical applications. AROSICS, a novel algorithm for multi-sensor image co-registration and geometric harmonization, provides a robust and automated detection and correction of positional shifts and aligns the data to a common coordinate grid. The second algorithm, SpecHomo, was developed to unify differing spectral sensor characteristics. It relies on separate material-specific regressors for different land cover classes enabling higher transformation accuracies and the estimation of unilaterally missing spectral bands. Based on these algorithms, a third study investigated the added value of synthesized red edge bands and the use of dense time series, enabled by sensor fusion, for the estimation of burn severity and mapping of fire damage from Landsat. The results illustrate the effectiveness of the developed algorithms to reduce multi-sensor, multi-temporal data inconsistencies and demonstrate the added value of geometric and spectral harmonization for subsequent products. Synthesized red edge information has proven valuable when retrieving vegetation-related parameters such as burn severity. Moreover, using sensor fusion for combining multi-sensor time series was shown to offer great potential for more accurate monitoring and mapping of quickly evolving environmental processes