Performance Evaluation of a Multispectral Classificator that Employs High-Performance Computing Techniques

The classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate image classification approach which is based on multispectral sets and uses pixel statistics for the class description, and it is referred to as the Multispectral Pixel Classification method. This paper presents a study of the performance that this approach provides for supervised segmentation and classification of sensed signatures for land use analysis and using high- performance computing techniques compared with traditional programming methodologies. The results obtained with this study uses real multispectral scenes obtained with remote sensing techniques (high-resolution optical images) to probe the efficiency of the classification technique.ITESO, A.C

A Dynamical Model to Classify the Content of Multitemporal Images employing Distributed Computing Techniques

An intelligent post-processing paradigm based on the use of a dynamical filtering technique modified to enhance the reconstruction quality of remote sensing indexes using multitemporal images and distributed computing techniques is proposed. As a matter of particular study, a robust algorithm is reported for the analysis of the dynamic behavior of geophysical signatures extracted from remotely sensed scenes. The simulation results prove the efficiency of the proposed technique along with the computational implementation based on a big-data framework using distributed processing.ITESO, A.C

Classification Algorithm for Embedded Systems using High-Resolution Multispectral Data

The extraction of remote sensing signatures from a particular geographical region allows the generation of electronic signature maps, which are the basis to create a high-resolution collection atlas processed in discrete time. This can be achieved using an image classification approach based on pixel statistics for the class description, referred to as the multispectral pixel neighborhood method. This paper explores the effectiveness of this approach developed for supervised segmentation and classification of high- resolution remote sensing imagery using SPOT-5 data. Moreover, an analysis of the proposition for implementation as an embedded system is provided, to improve the processing time and reducing computational load, using a scheme based on hardware/software codesign techniques. Simulations are reported to probe the efficiency of the proposed technique.ITESO, A.C

Dynamical Analysis of Hydrological Indexes Extracted from Remote Sensing Imagery: An Introductory Study

A new intelligent computational paradigm based on filtering techniques modified to enhance the quality of reconstruction of the physical characteristics of environmental electronic maps extracted from the large scale remote sensing imagery is proposed. First, the problem-oriented modification of the previously proposed fused Bayesian-regularization enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures of interest. Second, the extraction of the so-called hydrological electronic maps and the analysis of its dynamics are proposed. Finally, simulation results of hydrological remote sensing signatures reconstruction from enhanced real-world environmental images are reported to verify the efficiency of the proposed approach.CINVESTA

Identification Model for Large Remote Sensing Datasets Applied to Environmental Analysis within Mexico

The classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate identification model that is based on multispectral data and uses pixel statistics for the class description. This methodology is referred to as the Multispectral Identification Model. This paper presents this particular methodology applied to large remote sensing datasets (multispectral images obtained from the SPOT-5 satellite sensors) with the objective to perform environmental and land use analysis for regions within Mexico, taking advantage of high-performance computing techniques to improve the processing time and computational load. The results obtained uses real multispectral scenes (high- resolution optical images) to probe the efficiency of the classification technique

Dynamical Characterization of Land-Use Classification using Multispectral Remote Sensing Data for Guadalajara Region

An intelligent post-processing computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of remote sensing signatures based on SPOT-5 imagery is proposed. As a matter of particular study, a robust algorithm is reported for the analysis of the dynamic behavior of geophysical indexes extracted from remotely sensed scenes. Simulations are reported to probe the efficiency of the proposed technique.ITESO, A.C

Intelligent Processing for SAR Imagery for Environmental Management

A new intelligent computational paradigm based on the use of Kalman filtering technique [4] modified to reconstruct the dynamic behavior of the physical and electrical characteristics provided via reconstructive SAR imagery. As a matter of particular study, we develop and report the Kalman filter-based algorithm for high-resolution intelligent filtration of the dynamic behavior of the hydrological indexes of the particular tested remotely sensed scenes. The simulation results verify the efficiency of the proposed approach as required for decision support in environmental resources management.Cinvesta

Filtration and Enhancement of Environmental Characteristics Extracted from SAR Imagery Using Dynamic Kalman Technique

In this study, we propose a new computational paradigm based on the use of the Kalman filtering technique adjusted to reconstruct the dynamic behavior of the physical and electrical characteristics of different environmental monitoring data provided via reconstructive SAR imagery. As a matter of particular study we develop and report the Kalman filter-based algorithm for high-resolution filtration of the dynamic behavior of the hydrological indexes of the particular real-world SAR images of the test remotely sensed scenes. The simulation results verify the efficiency of the proposed approach.Cinvesta

Urban Knowledge Analysis for Dynamic Forecasting using Multispectral Data

The analysis of dynamical models for urban knowledge analysis using the information extracted from a geographical region processed from the data provided by multispectral remote sensing systems provides useful information for urban planning and resource management. However, several topics of interest on this particular matter are still to be properly studied. Using the remote sensing data that has been extracted from multispectral images from a particular geographic region in discrete time, its dynamic study is performed in both, spatial resolution and time evolution, in order to obtain the dynamical model of the physical variables and the evolutionary information about the data. This provides a background for understanding the future trends in development of the dynamics inherent in the multispectral and high-resolution images. This proposition is performed via an intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of the data extracted from multispectral remote sensing images and using high- performance computational techniques to unify the available data scheme with its dynamic analysis and, therefore, provide a behavioral model of the sensed data

Cognitive Reconstructive Remote Sensing for Decision Support in Environmental Resource Management

In this paper, the problem of reconstruction of different characteristic signatures (CSs) of the monitored environmental scenes from the multi-spectral remotely sensed data is cast in the unified framework of the statistically optimal Bayesian inference making strategy aggregated with the proposed cognitive descriptive regularization paradigm. The reconstructed CS maps are then treated as sufficient statistical data required for performing the environmental resource management tasks. Simulation examples with the real-world remote sensing data are provided to illustrate the efficiency of the proposed approach.Cinvesta