Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts. In particular, using the Tikhonov and Huber regularization in the derivative space, the promise of the proposed framework is demonstrated in static downscaling and fusion of synthetic multi-sensor precipitation data, while a data assimilation numerical experiment is presented using the heat equation in a variational setting

Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging

In this paper, we perform a comparative study of two recently proposed high-resolution radar imaging paradigms: the descriptive experiment design regularization (DEDR) and the fused Bayesian regularization (FBR) methods. The first one, the DEDR, employs aggregation of the descriptive regularization and worst-case statistical performance (WCSP) optimization approaches to enhanced radar/SAR imaging. The second one, the FBR, performs image reconstruction as a solution of the ill- conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties via unifying the Bayesian minimum risk (MR) estimation strategy with the maximum entropy (ME) randomized a priori image model and other projection-type regularization constraints imposed on the solution. Although the DEDR and the FBR are inferred from different descriptive and statistical constrained optimization paradigms, we examine how these two methods lead to structurally similar techniques that may be further transformed into new computationally more efficient robust adaptive imaging methods that enable one to derive efficient and consistent estimates of the SSP via unifying both the robust DEDR and FBR considerations. We present the results of extended comparative simulation study of the family of the image formation/ enhancement algorithms that employ the proposed robustified FBR and DEDR methods for high-resolution reconstruction of the SSP in a virtually real time. The computational complexity of different methods are analyzed and reported together with the scene imaging protocols. The advantages of the well designed SAR imaging experiments (that employ the FBR-based and DEDR-related robust estimators) over the cases of poorer designed experiments (that employ the conventional matched spatial filtering as well as the leaCinvestavUniversidad de Guadalajar

A Survey on Hybrid Techniques Using SVM

Support Vector Machines (SVM) with linear or nonlinear kernels has become one of the most promising learning algorithms for classification as well as for regression. All the multilayer perceptron (MLP),Radial Basic Function(RBF) and Learning Polynomials are also worked efficiently with SVM. SVM is basically derived from statistical Learning Theory and it is very powerful statistical tool. The basic principal for the SVM is structural risk minimization and closely related to regularization theory. SVM is a group of supervised learning techniques or methods, which is used to do for classification or regression. In this paper discussed the importance of Support Vector Machines in various areas. This paper discussing the efficiency of SVM with the combination of other classification techniques

Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations

Lokaalstatistikute kasutamine rohumaade ja metsade kaugseires

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolev doktoritöö analüüsib lokaalstatistikute kasutamist rohumaade ja metsade kaugseires. Töö esimene osa käsitleb rohumaade monitoorimist tehisava-radari (synthetic aperture radar (SAR)) abil ning teine osa metsade kaugseiret kasutades optilisi sensoreid. Analüüsides rohumaade niitmise ja C- laineala tehisava-radari interferomeetrilise koherentsuse seoseid leiti, et selle parameetri kasutamisel on potentsiaali niitmise tuvastamise algoritmide ja rakenduste väljaarendamiseks. Tulemused näitavad, et pärast niitmist on VH ja VV polarisatsiooni 12-päeva interferomeetrilise koherentsuse mediaan väärtused statistiliselt oluliselt kõrgemad võrreldes niitmise eelse olukorraga. Koherentsus on seda kõrgem, mida väiksem on ajaline vahe niitmise ja pärast seda üles võetud esimese interferomeetrilise mõõtmise vahel. Hommikune kaste, sademed, põllutööde teostamine, näiteks külv või kündmine, kõrgelt niitmine ja kiire rohu kasv pärast niitmist vähendavad koherentsust ja raskendavad niitmise sündmuste eristamist. Selleks, et eelpoolnimetatud mõjusid leevendada tuleks tulevikus uurida 6-päeva koherentsuse ja niitmise sündmuste vahelisi seoseid. Käesolevas doktoritöös esitatud tulemused loovad siiski tugeva aluse edasisteks uuringuteks ja arendusteks eesmärgiga võtta C-laineala tehisava-radari andmed niitmise tuvastamisel ka praktikas kasutusele. Lisaks näidati, et ortofotodel põhinevate metsa kaugseire hinnangute andmisel on abi lokaalstatistikute kasutamisest. Analüüsides kaugseire hinnangut riigimetsa takseerandmete (national forest inventory) kohta leiti, et näidistel põhinev järeldamine (case-based reasoning (CBR)) sobib hästi selliste kaugseire ülesannete empiirilisteks lahendusteks, kus sisendandmetena on kasutatavad väga paljud erinevad andmeallikad. Leiti, et klasteranalüüsi saab kasutada kaugseire tunnuste eelvaliku meetodina. Võrreldes erinevaid tekstuuri statistikuid näidati, et lokaalselt arvutatud keskväärtus on kõige väärtuslikum tunnus. Järeldati, et nii statistiliste kui ka struktuursete lokaalstatistikute kasutamisega saab lisada pikslipõhistele kaugseire hinnangutele olulist andmestikku.This thesis studies approaches for remote sensing of grasslands and forests based on local statistics. The first part of the thesis focuses on monitoring of grasslands with SAR and the second part to monitoring of forests with optical sensors. It is shown that there is potential to develop mowing detection algorithms and applications using C-band SAR temporal interferometric coherence. The results demonstrate that after a mowing event, median VH and VV polarisation 12-day interferometric coherence values are statistically significantly higher than those from before the event. The sooner after the mowing event the first interferometric acquisition is taken, the higher the coherence. Morning dew, precipitation, farming activities, such as sowing or ploughing, high residual straws after the cut and rapid growth of grass are causing the coherence to decrease and impede the distinction of a mowing event. In the future, six-day interferometric coherence should also be analysed in relation to mowing events to alleviate some of these factors. Nevertheless, the results presented in this thesis offer a strong basis for further research and development activities towards the practical use of spaceborne C-band SAR data for mowing detection. Further, it was shown that local statistics can be useful for estimation of forest parameters from ortophotos and they could also provide helpful ancillary information to conduct a photo-interpretation tasks over forested areas. It was demonstrated that cluster analysis can be used as pre-selection method for the reduction of remote sensing features. Additionally, it was shown that case-based reasoning (a machine learning method) is well suited for empirical solutions of remote sensing tasks where there are many different data sources available. It was concluded that the use of local statistics adds valuable data to pixel-based remote sensing estimations

Remote Sensing Imagery and Signature Fields Reconstruction via Aggregation of Robust Regularization With Neural Computing

The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. First, the problem-oriented modification of the previously proposed fused Bayesian- regularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. Second, the modification of the Hopfield-type maximum entropy neural network (NN) is proposed that enables such NN to perform numerically the robust adaptive FBR technique via efficient NN computing. Finally, we report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the devel- oped method.Cinvesta

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively