Data-driven localization mappings in filtering the monsoon-Hadley multicloud convective flows

This paper demonstrates the efficacy of data-driven localization mappings for assimilating satellite-like observations in a dynamical system of intermediate complexity. In particular, a sparse network of synthetic brightness temperature measurements is simulated using an idealized radiative transfer model and assimilated to the monsoon-Hadley multicloud model, a nonlinear stochastic model containing several thousands of model coordinates. A serial ensemble Kalman filter is implemented in which the empirical correlation statistics are improved using localization maps obtained from a supervised learning algorithm. The impact of the localization mappings is assessed in perfect model observing system simulation experiments (OSSEs) as well as in the presence of model errors resulting from the misspecification of key convective closure parameters. In perfect model OSSEs, the localization mappings that use adjacent correlations to improve the correlation estimated from small ensemble sizes produce robust accurate analysis estimates. In the presence of model error, the filter skills of the localization maps trained on perfect and imperfect model data are comparable.Comment: monthly weather review (in press

Dimensionality Reduction via Regression in Hyperspectral Imagery

This paper introduces a new unsupervised method for dimensionality reduction via regression (DRR). The algorithm belongs to the family of invertible transforms that generalize Principal Component Analysis (PCA) by using curvilinear instead of linear features. DRR identifies the nonlinear features through multivariate regression to ensure the reduction in redundancy between he PCA coefficients, the reduction of the variance of the scores, and the reduction in the reconstruction error. More importantly, unlike other nonlinear dimensionality reduction methods, the invertibility, volume-preservation, and straightforward out-of-sample extension, makes DRR interpretable and easy to apply. The properties of DRR enable learning a more broader class of data manifolds than the recently proposed Non-linear Principal Components Analysis (NLPCA) and Principal Polynomial Analysis (PPA). We illustrate the performance of the representation in reducing the dimensionality of remote sensing data. In particular, we tackle two common problems: processing very high dimensional spectral information such as in hyperspectral image sounding data, and dealing with spatial-spectral image patches of multispectral images. Both settings pose collinearity and ill-determination problems. Evaluation of the expressive power of the features is assessed in terms of truncation error, estimating atmospheric variables, and surface land cover classification error. Results show that DRR outperforms linear PCA and recently proposed invertible extensions based on neural networks (NLPCA) and univariate regressions (PPA).Comment: 12 pages, 6 figures, 62 reference

Blind Identification of SIMO Wiener Systems based on Kernel Canonical Correlation Analysis

We consider the problem of blind identification and equalization of single-input multiple-output (SIMO) nonlinear channels. Specifically, the nonlinear model consists of multiple single-channel Wiener systems that are excited by a common input signal. The proposed approach is based on a well-known blind identification technique for linear SIMO systems. By transforming the output signals into a reproducing kernel Hilbert space (RKHS), a linear identification problem is obtained, which we propose to solve through an iterative procedure that alternates between canonical correlation analysis (CCA) to estimate the linear parts, and kernel canonical correlation (KCCA) to estimate the memoryless nonlinearities. The proposed algorithm is able to operate on systems with as few as two output channels, on relatively small data sets and on colored signals. Simulations are included to demonstrate the effectiveness of the proposed technique

Optimized Kernel Entropy Components

This work addresses two main issues of the standard Kernel Entropy Component Analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of by variance as in Kernel Principal Components Analysis. In this work, we propose an extension of the KECA method, named Optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the Independent Component Analysis (ICA) framework, and introduces an extra rotation to the eigen-decomposition, which is optimized via gradient ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both methods is the selection of the kernel parameter since it critically affects the resulting performance. Here we analyze the most common kernel length-scale selection criteria. Results of both methods are illustrated in different synthetic and real problems. Results show that 1) OKECA returns projections with more expressive power than KECA, 2) the most successful rule for estimating the kernel parameter is based on maximum likelihood, and 3) OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.Comment: IEEE Transactions on Neural Networks and Learning Systems, 201

Cooperative localization using angle of arrival measurements: sequential algorithms and non-line-of-sight suppression

We investigate localization of a source based on angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers. The goal is to efficiently compute accurate estimates despite outliers in the AoA measurements due to multipath reflections in non-line-of-sight (NLOS) environments. Maximal likelihood (ML) location estimation in such a setting requires exhaustive testing of estimates from all possible subsets of "good" measurements, which has exponential complexity in the number of measurements. We provide a randomized algorithm that approaches ML performance with linear complexity in the number of measurements. The building block for this algorithm is a low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments. Our Bayesian framework can exploit the ability to resolve multiple paths in wideband systems to provide significant performance gains over narrowband systems in NLOS environments, and easily extends to accommodate additional information such as range measurements and prior information about location.Comment: 31 pages, 11 figures, related to MELT'08 Workshop proceedin

EVALUATING SATELLITE DERIVED BATHYMETRY IN REGARD TO TOTAL PROPAGATED UNCERTAINTY, MULTI-TEMPORAL CHANGE DETECTION, AND MULTIPLE NON-LINEAR ESTIMATION

Acoustic and electromagnetic hydrographic surveys produce highly-accurate bathymetric data that can be used to update and improve current nautical charts. For shallow-water surveys (i.e., less than 50m depths), this includes the use of single-beam echo-sounders (SBES), multi-beam echo-sounders (MBES), and airborne lidar bathymetry (ALB). However, these types of hydrographic surveys are time-consuming and require considerable financial and operational resources to conduct. As a result, some maritime regions are seldom surveyed due to their remote location and challenging logistics. Satellite-derived bathymetry (SDB) provides a means to supplement traditional acoustic hydrographic surveys. In particular, Landsat 8 imagery: 1) provides complete coverage of the Earth’s surface every 16 days, 2) has an improved dynamic range (12-bits), and 3) is freely-available from the US Geological Survey. While the 30 m spatial resolution does not match MBES, ALB, or SBES coverage, SDB based on Landsat 8 can be regarded as a type of “reconnaissance survey” that can be used to identify potential hazards to navigation in areas that are seldom surveyed. It is also a useful means to monitor change detection in dynamic regions. This study focused on developing improved image-processing techniques and time-series analysis for SDB from Landsat 8 imagery for three different applications: 1. An improved means to estimate total propagated uncertainty (TPU), mainly the vertical component, for single-image SDB; 2. Identifying the location and movement of dynamic shallow areas in river entrances based on multiple-temporal Landsat 8 imagery; 3. Using a multiple, nonlinear SDB approach to enhance depth estimations and enable bottom discrimination. An improved TPU estimation was achieved based on the two most common optimization approaches (Dierssen et al., 2003 and Stumpf et al., 2003). Various single-image SDB band-ratio outcomes and associated uncertainties were compared against ground truth (i.e., recent Lidar surveys). Several parameters were tested, including various types of filters, kernel sizes, number of control points and their coverage, and recent vs. outdated control points. Based on the study results for two study sites (Cape Ann, MA and Ft Myers, FL), similar performance was observed for both the Stumpf and the Dierssen models. Validation was performed by comparing estimated depths and uncertainties to observed ALB data. The best performing configuration was achieved using low-pass filter (kernel size 3x3) with ALB control points that were distributed over the entire study site. A change detection process using image processing was developed to identify the location and movement of dynamic shallow areas in riverine environments. Yukon River (Alaska) and Amazon River (Brazil) entrances were evaluated as study sites using multiple satellite imagery. A time-series analysis was used to identify probable shallow areas with no usable control points. By using an SDB ratio model with image processing techniques that includes feature extraction and a well-defined topological feature to describe the shoal feature, it is possible to create a time-series of the shoal’s motion, and predict its future location. A further benefit of this approach is that vertical referencing of the SDB ratio model to chart datum is not required. In order to enhance the capabilities of the SDB approach to estimate depth in non-uniform conditions, Dierssen’s band ration SDB algorithm was transformed into a full non-linear SDB model. The model was evaluated in the Simeonof Island, AK, using Lidar control points from a previous NOAA ALB survey. Linear and non-linear SDB models were compared using the ALB survey for performance evaluation. The multi-nonlinear SDB model provides an enhanced performance compared to the more traditional linear SDB method. This is most noticeable in the very shallow waters (0-2 m), where a linear model does not provide a good correlation to the control points. In deep-waters close to the extinction depth, the multi-nonlinear SDB method is also able to better detect bottom features than the linear SDB method. By recognizing the water column contributions to the SDB solution, it is possible to achieve a more accurate estimate of the bathymetry in remote areas

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information SystemsImaging and sensing technologies are constantly evolving so that, now, the latest generations of satellites commonly provide with Earth’s surface snapshots at very short sampling periods (i.e. daily images). It is unquestionable that this tendency towards continuous time observation will broaden up the scope of remotely sensed activities. Inevitable also, such increasing amount of information will prompt methodological approaches that combine digital image processing techniques with time series analysis for the characterization of land cover distribution and monitoring of its dynamics on a frequent basis. Nonetheless, quantitative analyses that convey the proficiency of three-dimensional satellite images data sets (i.e. spatial, spectral and temporal) for the automatic mapping of land cover and land cover time evolution have not been thoroughly explored. In this dissertation, we investigate the usefulness of multispectral time series sets of medium spatial resolution satellite images for the regular land cover characterization at the national scale. This study is carried out on the territory of Continental Portugal and exploits satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and MEdium Resolution Imaging Spectrometer (MERIS). In detail, we first focus on the analysis of the contribution of multitemporal information from multispectral satellite images for the automatic land cover classes’ discrimination. The outcomes show that multispectral information contributes more significantly than multitemporal information for the automatic classification of land cover types. In the sequence, we review some of the most important steps that constitute a standard protocol for the automatic land cover mapping from satellite images. Moreover, we delineate a methodological approach for the production and assessment of land cover maps from multitemporal satellite images that guides us in the production of a land cover map with high thematic accuracy for the study area. Finally, we develop a nonlinear harmonic model for fitting multispectral reflectances and vegetation indices time series from satellite images for numerous land cover classes. The simplified multitemporal information retrieved with the model proves adequate to describe the main land cover classes’ characteristics and to predict the time evolution of land cover classes’individuals

An Overview on Integrated Localization and Communication Towards 6G

While the 5G cellular system is being deployed worldwide, researchers have started the investigation of the 6G mobile communication networks. Although the essential requirements and key usage scenarios of 6G are yet to be defined, it is believed that 6G should be able to provide intelligent and ubiquitous wireless connectivity with Tbps data rate and sub-millisecond latency over 3D network coverage. To achieve such goals, acquiring accurate location information of the mobile terminals is becoming extremely useful, not only for location-based services but also for improving wireless communication performance in various ways such as channel estimation, beam alignment, medium access control, routing, and network optimization. On the other hand, the advancement of communication technologies also brings new opportunities to greatly improve the localization performance, as exemplified by the anticipated centimeter-level localization accuracy in 6G by ultra massive MIMO and mmWave technologies. In this regard, a unified study on integrated localization and communication (ILAC) is necessary to unlock the full potential of wireless networks for the best utilization of network infrastructure and radio resources for dual purposes. While there are extensive literatures on wireless localization or communications separately, the research on ILAC is still in its infancy. Therefore, this article aims to give a tutorial overview on ILAC towards 6G wireless networks. After a holistic survey on wireless localization basics, we present the state-of-the-art results on how wireless localization and communication inter-play with each other in various network layers, together with the main architectures and techniques for localization and communication co-design in current 2D and future 3D networks with aerial-ground integration. Finally, we outline some promising future research directions for ILAC.Comment: 35 pages, 18 figure

A Survey on Object Detection in Optical Remote Sensing Images

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey 1) template matching-based object detection methods, 2) knowledge-based object detection methods, 3) object-based image analysis (OBIA)-based object detection methods, 4) machine learning-based object detection methods, and 5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.Comment: This manuscript is the accepted version for ISPRS Journal of Photogrammetry and Remote Sensin