Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion

The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hb201

Online Graph-Based Change Point Detection in Multiband Image Sequences

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method

Investigation related to multispectral imaging systems

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community

AUTONOMOUS SURFACE DETECTION AND TRACKING FOR FMCW SNOW RADAR USING FIELD PROGRAMMABLE GATE ARRAYS

Sea Ice in Polar Regions is typically covered with a layer of snow. The thermal insulation properties and high albedo of the snow cover insulates the sea ice beneath it, maintaining low temperatures and limiting ice melt, and thus affecting sea ice thickness and growth rates. Remote sensing of snow cover thickness plays a major role in understanding the mass balance of sea ice, inter-annual variability of snow depth, and other factors which directly impact climate change. Researchers at the Center for Remote Sensing of Ice Sheets (CReSIS) at University of Kansas have developed an ultra-wide band FMCW Snow Radar used to measure snow thickness and map internal layers of polar firn from low and high-altitude. This system has shown outstanding performance, but it has some limitations in terms of operational altitude and relies on the operator to make adjustments during surveys to capture radar echoes if the altitude changes significantly. In this thesis, an automated onboard real-time surface tracker for the snow radar is presented to detect snow surface elevation from the aircraft and track changes in the surface elevation. A common technique for an FMCW radar to have a long-range (high-altitude) capability relies on the system’s ability to delay the reference chirp signal used for de-chirping to maintain a relatively constant beat frequency. Currently, the radar uses an analog filter bank to condition the received IF signal over discrete altitude ranges and store the spectral power in each band utilizing different Nyquist zones. During airborne missions in Polar Regions with the radar, the operator has to manually switch the filter banks whenever there is a significant change in aircraft elevation. The work done in this thesis aims at eliminating the manual switching operation and providing the radar with surface detection, chirp delay, and a constant beat frequency feedback loop to enhance its long-range capability and ensure autonomous operation

Temporal constraints on linear BRDF model parameters

Linear models of bidirectional reflectance distribution are useful tools for understanding the angular variability of surface reflectance as observed by medium-resolution sensors such as the Moderate Resolution Imaging Spectrometer. These models are operationally used to normalize data to common view and illumination geometries and to calculate integral quantities such as albedo. Currently, to compensate for noise in observed reflectance, these models are inverted against data collected during some temporal window for which the model parameters are assumed to be constant. Despite this, the retrieved parameters are often noisy for regions where sufficient observations are not available. This paper demonstrates the use of Lagrangian multipliers to allow arbitrarily large windows and, at the same time, produce individual parameter sets for each day even for regions where only sparse observations are available

An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors

Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies

abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Atmospheric remote sensing and radiopropagation: from numerical modeling to spaceborne and terrestrial applications

The remote sensing of electromagnetic wave properties is probably the most viable and fascinating way to observe and study physical media, comprising our planet and its atmosphere, at the same time ensuring a proper continuity in the observations. Applications are manifold and the scientific community has been importantly studying and investing on new technologies, which would let us widen our knowledge of what surrounds us. This thesis aims at showing some novel techniques and corresponding applications in the field of the atmospheric remote sensing and radio-propagation, at both microwave and optical wavelengths. The novel Sun-tracking microwave radiometry technique is shown. The antenna noise temperature of a ground-based microwave radiometer is measured by alternately pointing toward-the-Sun and off-the-Sun while tracking it along its diurnal ecliptic. During clear sky the brightness temperature of the Sun disk emission at K and Ka frequency bands and in the under-explored millimeter-wave V and W bands can be estimated by adopting different techniques. Parametric prediction models for retrieving all-weather atmospheric extinction from ground-based microwave radiometers are tested and their accuracy evaluated. Moreover, a characterization of suspended clouds in terms of atmospheric path attenuation is presented, by exploiting a stochastic approach used to model the time evolution of the cloud contribution. A model chain for the prediction of the tropospheric channel for the downlink of interplanetary missions operating above Ku band is proposed. On top of a detailed description of the approach, the chapter presents the validation results and examples of the model-chain online operation. Online operation has already been tested within a feasibility study applied to the BepiColombo mission to Mercury operated by the European Space Agency (ESA) and by exploiting the Hayabusa-2 mission Ka-band data by the Japan Aerospace Exploration Agency (JAXA), thanks to the ESA cross-support service. A preliminary (and successful) validation of the model-chain has been carried out by comparing the simulated signal-to-noise ratio with the one received from Hayabusa-2. At the next ITU World Radiocommunication Conference 2019, Agenda Item 1.13 will address the identification and the possible additional allocation of radio-frequency spectrum to serve the future development of systems supporting the fifth generation of cellular mobile communications (5G). The potential impact of International Mobile Telecommunications (IMT) deployments is shown in terms of received radio frequency interference by ESA’s telecommunication links. Received interference can derive from several radio-propagation mechanisms, which strongly depend on atmospheric conditions, radio frequency, link availability, distance and path topography; at any time a single mechanism, or more than one may be present. Results are shown in terms of required separation distances, i.e. the minimum distance between the earth station and the IMT station ensuring that the protection criteria for the earth station are met