MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...ijaia
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
Alta dirección: Nuevo paradigma en recolección de residuos urbanosHéctor Debernardo
Este artículo, escrito por la Dra. Margarita Hurtado Hernández y el Dr. Héctor D. Debernardo, fue presentado y aceptado en dos congresos internacionales, uno organizado por la System Dynamics Society y otro organizado por la International Society for the Systems Science.
Es una síntesis de la tesis doctoral de la Dra. Hurtado Hernández, en la cual colaboró el Dr. Debernardo.
Este documento es un ejemplo de cómo se puede innovar en las operaciones de la empresa de manera de tener ventajas competitivas.
El Dr. Debernardo enseña y ayuda a implementar este conocimiento en sus actividades de dirección de empresas, asesoría a la alta dirección y mentoría de ejecutivos y sucesores. La Dra. Hurtado Hernández lo difunde y aplica en sus actividades académicas de docencia universitaria e investigación.
A SWITCHED-ANTENNA NADIR-LOOKING INTERFEROMETRIC SAR ALTIMETER FOR TERRAIN-AI...csandit
Conventional terrain-aided navigation (TAN) technique uses an altimeter to locate the position of an aerial vehicle. However, a major problem with a radar altimeter is that its beam (or pulse) footprint on the ground could be large, and therefore the nadir altitude cannot be estimated
accurately. To overcome this difficulty, one may use the nadir-looking synthetic aperture radar (SAR) technique to reduce the along-track beam width, while the cross-track ambiguity is
resolved with the interferometry technique. However, the cross-track resolution is still far from satisfactory, because of the limited aperture size of antennas. Therefore, the usual three-antenna array cannot resolve multiple terrain points in a same range bin, effectively. In this paper, we
propose a technique that can increase the cross-track resolution using a large number of antennas, but in a switched fashion, not raising hardware cost.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...ijaia
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
Alta dirección: Nuevo paradigma en recolección de residuos urbanosHéctor Debernardo
Este artículo, escrito por la Dra. Margarita Hurtado Hernández y el Dr. Héctor D. Debernardo, fue presentado y aceptado en dos congresos internacionales, uno organizado por la System Dynamics Society y otro organizado por la International Society for the Systems Science.
Es una síntesis de la tesis doctoral de la Dra. Hurtado Hernández, en la cual colaboró el Dr. Debernardo.
Este documento es un ejemplo de cómo se puede innovar en las operaciones de la empresa de manera de tener ventajas competitivas.
El Dr. Debernardo enseña y ayuda a implementar este conocimiento en sus actividades de dirección de empresas, asesoría a la alta dirección y mentoría de ejecutivos y sucesores. La Dra. Hurtado Hernández lo difunde y aplica en sus actividades académicas de docencia universitaria e investigación.
A SWITCHED-ANTENNA NADIR-LOOKING INTERFEROMETRIC SAR ALTIMETER FOR TERRAIN-AI...csandit
Conventional terrain-aided navigation (TAN) technique uses an altimeter to locate the position of an aerial vehicle. However, a major problem with a radar altimeter is that its beam (or pulse) footprint on the ground could be large, and therefore the nadir altitude cannot be estimated
accurately. To overcome this difficulty, one may use the nadir-looking synthetic aperture radar (SAR) technique to reduce the along-track beam width, while the cross-track ambiguity is
resolved with the interferometry technique. However, the cross-track resolution is still far from satisfactory, because of the limited aperture size of antennas. Therefore, the usual three-antenna array cannot resolve multiple terrain points in a same range bin, effectively. In this paper, we
propose a technique that can increase the cross-track resolution using a large number of antennas, but in a switched fashion, not raising hardware cost.
GPS cycle slips detection and repair through various signal combinationsIJMER
Abstract: GPS Cycle slips affect the measured spatial distance between the satellite and the receiver, thus affecting the accuracy of the derived 3D coordinates of any ground station. Therefore, cycle slips must be detected and repaired before performing any data processing. The objectives of this research are to detect the Cycle slips by using various types of GPS signal combinations with graphical and statistical tests techniques, and to repair cycle slips by using average and time difference geometry techniques. Results of detection process show that the graphical detection can be used as a primary detection
technique whereas the statistical approaches of detection are proved to be superior. On the other hand, results of repairing process show that any trial can be used for such process except for the 1st and 2nd time differences averaging all data as they give very low accuracy of the cycle slip fixation.
A Novel Approach for Ship Recognition using Shape and Texture ijait
Maritime security includes reliable identification of ship entering and leaving a nation’s territorial waters. Sea target detection from remote sensing imagery is very important, with a wide array of applications in areas such as fishery management, vessel traffic services, and naval warfare. Automated systems that could identify a ship could complement existing electronic signal identification methods. A new classification approach using shape and texture is introduced for Ship detection. Texture information can improve classification performance. This approach uses both shape and texture features. Feature extraction is done by Hu’s moment invariants with several classification algorithms. This paper presents an overview of
automatic ship recognition methods with a view towards embedded implementation on optical smart cameras. Therefore this approach may attain a good classification rate.
Compressive sampling (CS) aims at acquiring a signal at a sampling rate below the Nyquist rate by exploiting prior knowledge that a signal is sparse or correlated in some domain. Despite the remarkable progress in the theory of CS, the sampling rate on a single image required by CS is still very high in practice. In this presentation, a non-local compressive sampling (NLCS) recovery method is proposed to further reduce the sampling rate by exploiting non-local patch correlation and local piecewise smoothness present in natural images. Two non-local sparsity measures, i.e., non-local wavelet sparsity and non-local joint sparsity, are proposed to exploit the patch correlation in NLCS. An efficient iterative algorithm is developed to solve the NLCS recovery problem, which is shown to have stable convergence behavior in experiments. The experimental results show that our NLCS significantly improves the state-of-the-art of image compressive sampling.
Performance evaluation of telemetry stations based on site selectionPriyasloka Arya
In a test range, selection of sites for deployment of mobile telemetry stations plays a crucial role for
acquiring and tracking any airborne vehicle under test. Efforts have been made to correlate the
tracking performance of the auto track stations based on site selection for various test flights conducted from different launching pads.
Some of the tracking methodologies discussed in this paper are single- channel amplitude
comparison monopulse (SCACMP) technique and E-SCAN technique.
Also, the performance of a simple telemetry data acquisition system using helical antenna is compared
with these auto track stations.
Atmospheric Correction of Remotely Sensed Images in Spatial and Transform DomainCSCJournals
Remotely sensed data is an effective source of information for monitoring changes in land use and land cover. However remotely sensed images are often degraded due to atmospheric effects or physical limitations. Atmospheric correction minimizes or removes the atmospheric influences that are added to the pure signal of target and to extract more accurate information. The atmospheric correction is often considered critical pre-processing step to achieve full spectral information from every pixel especially with hyperspectral and multispectral data. In this paper, multispectral atmospheric correction approaches that require no ancillary data are presented in spatial domain and transform domain. We propose atmospheric correction using linear regression model based on the wavelet transform and Fourier transform. They are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The application of the atmospheric correction methods for vegetation analyses using Normalized Difference Vegetation Index is also presented in this paper.
Routage dans les réseaux de capteurs segonde partie Tuenkam Steve
Routage dans les réseaux de capteurs : rappel & brève présentation du routage opportuniste. Présentation de quelque slides sur GeRaF ( Geographic Random Forwarding), sur SPIN ( Sensor Protocol for Information Negociation), sur LEACH ( Low Energy Adaptative Clustering Hierarchy). En plus nous présentons aussi quelques domaines d'application des réseaux de capteurs.
GPS cycle slips detection and repair through various signal combinationsIJMER
Abstract: GPS Cycle slips affect the measured spatial distance between the satellite and the receiver, thus affecting the accuracy of the derived 3D coordinates of any ground station. Therefore, cycle slips must be detected and repaired before performing any data processing. The objectives of this research are to detect the Cycle slips by using various types of GPS signal combinations with graphical and statistical tests techniques, and to repair cycle slips by using average and time difference geometry techniques. Results of detection process show that the graphical detection can be used as a primary detection
technique whereas the statistical approaches of detection are proved to be superior. On the other hand, results of repairing process show that any trial can be used for such process except for the 1st and 2nd time differences averaging all data as they give very low accuracy of the cycle slip fixation.
A Novel Approach for Ship Recognition using Shape and Texture ijait
Maritime security includes reliable identification of ship entering and leaving a nation’s territorial waters. Sea target detection from remote sensing imagery is very important, with a wide array of applications in areas such as fishery management, vessel traffic services, and naval warfare. Automated systems that could identify a ship could complement existing electronic signal identification methods. A new classification approach using shape and texture is introduced for Ship detection. Texture information can improve classification performance. This approach uses both shape and texture features. Feature extraction is done by Hu’s moment invariants with several classification algorithms. This paper presents an overview of
automatic ship recognition methods with a view towards embedded implementation on optical smart cameras. Therefore this approach may attain a good classification rate.
Compressive sampling (CS) aims at acquiring a signal at a sampling rate below the Nyquist rate by exploiting prior knowledge that a signal is sparse or correlated in some domain. Despite the remarkable progress in the theory of CS, the sampling rate on a single image required by CS is still very high in practice. In this presentation, a non-local compressive sampling (NLCS) recovery method is proposed to further reduce the sampling rate by exploiting non-local patch correlation and local piecewise smoothness present in natural images. Two non-local sparsity measures, i.e., non-local wavelet sparsity and non-local joint sparsity, are proposed to exploit the patch correlation in NLCS. An efficient iterative algorithm is developed to solve the NLCS recovery problem, which is shown to have stable convergence behavior in experiments. The experimental results show that our NLCS significantly improves the state-of-the-art of image compressive sampling.
Performance evaluation of telemetry stations based on site selectionPriyasloka Arya
In a test range, selection of sites for deployment of mobile telemetry stations plays a crucial role for
acquiring and tracking any airborne vehicle under test. Efforts have been made to correlate the
tracking performance of the auto track stations based on site selection for various test flights conducted from different launching pads.
Some of the tracking methodologies discussed in this paper are single- channel amplitude
comparison monopulse (SCACMP) technique and E-SCAN technique.
Also, the performance of a simple telemetry data acquisition system using helical antenna is compared
with these auto track stations.
Atmospheric Correction of Remotely Sensed Images in Spatial and Transform DomainCSCJournals
Remotely sensed data is an effective source of information for monitoring changes in land use and land cover. However remotely sensed images are often degraded due to atmospheric effects or physical limitations. Atmospheric correction minimizes or removes the atmospheric influences that are added to the pure signal of target and to extract more accurate information. The atmospheric correction is often considered critical pre-processing step to achieve full spectral information from every pixel especially with hyperspectral and multispectral data. In this paper, multispectral atmospheric correction approaches that require no ancillary data are presented in spatial domain and transform domain. We propose atmospheric correction using linear regression model based on the wavelet transform and Fourier transform. They are tested on Landsat image consisting of 7 multispectral bands and their performance is evaluated using visual and statistical measures. The application of the atmospheric correction methods for vegetation analyses using Normalized Difference Vegetation Index is also presented in this paper.
Routage dans les réseaux de capteurs segonde partie Tuenkam Steve
Routage dans les réseaux de capteurs : rappel & brève présentation du routage opportuniste. Présentation de quelque slides sur GeRaF ( Geographic Random Forwarding), sur SPIN ( Sensor Protocol for Information Negociation), sur LEACH ( Low Energy Adaptative Clustering Hierarchy). En plus nous présentons aussi quelques domaines d'application des réseaux de capteurs.
How to scale threat modelling activities across many applications and large development teams using templates and risk patterns.
Introducing IriusRisk Community edition
Presentation given at O'Reilly Security Amsterdam 2016
Le « RUN » (ou la Tierce Maintenance Applicative)ekino
Après avoir mis en ligne un nouveau site web, on entre dans une phase dite de « RUN » ou plus précisément en Tierce Maintenance Applicative (TMA). Il s’agit d’assurer une maintenance corrective, mais pas seulement… Il faut prendre en compte la maintenance préventive, la maintenance adaptative, le support fonctionnel, l’exploitation, etc.
Or il y a souvent des confusions entre toutes ces notions. Je propose de partager leur définition et quelques bonnes pratiques de mise en œuvre, tout en illustrant la spécificité de chacune de ces maintenances à partir de situations empruntées aux classiques du cinéma.
In this talk, I show the coolest news about Polymer's new release. It starts with a brief introduction of what are Web Components and Polymer, after I show 1.0 features, like Shady DOM, Theming with CSS custom properties and the new catalogue of elements. And, to finish, I show a guide through the Polymer Starter Kit, talking about Material Design, Adaptative UIs and Offline first.
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Dee...gerogepatton
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators
for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine eflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West
Africa. These data fusion take into account the bias between case water and instruments. We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEE...gerogepatton
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m
A Richardson-Lucy Algorithm Using a Varying Point Spread Function Along the I...CSCJournals
Image restorations with the Richardson-Lucy algorithm suffer the usual drawback imposed by the constraint of a constant Point Spread Function - PSF as unfolding function. Indeed, even when the image exhibits a constant spatial resolution over its whole surface, an important aspect is that as the iterations advance, the overall resolution is improved while the PSF remains constant. This work proposes an algorithm which restores images by the Richardson-Lucy (RL) algorithm, using however, a varying PSF as the iterations proceed. For this purpose, the PSF width is reduced to cope with the last-achieved image resolution and the next iteration would be carried out with it. The process is repeated until the PSF does not change significantly. A main point in this procedure is how to evaluate the PSF tied to the image resolution. In this work this is performed on the grounds that the global contrast increases with the resolution improvement, for many gray pixels migrate towards darker or brighter regions. Hence, deconvolving an image with a steadily increasing PSF width, somewhere a maximum global contrast would be reached, corresponding to the best PSF. Synthetic, as well as experimental images deconvolved with the proposed technique, outperform the final quality of the same ones treated with the original Richardson-Lucy algorithm for any number of iterations. The algorithm and ancillary procedures have been embedded into an ad hoc written Fortran 90 program capable to generate synthetic images and process them and the real ones.
Investigation of Chaotic-Type Features in Hyperspectral Satellite Datacsandit
Hyperspectral images provide detailed spectral info
rmation with more than several hundred
channels. On the other hand, the high dimensionalit
y in hyperspectral images also causes to
classification problems due to the huge ratio betwe
en the number of training samples and the
features. In this paper, Lyapunov Exponents (LEs) a
re used to determine chaotic-type structure
of EO- 1 Hyperion hyperspectral image, a mixed fore
st site in Turkey. Experimental results
demonstrate that EO-1 Hyperion image has a chaotic
structure by checking distribution of
Lyapunov Exponents (LEs) and they can be used as d
iscriminative features to improve
classification accuracy for hyperspectral images.
Agu maosi chen h31g-1590 retrieval of surface ozone from uv-mfrsr irradiances...Maosi Chen
High concentration of surface ozone is harmful to humans and plants. USDA UV-B Monitoring and Research Program (UVMRP) uses Ultraviolet (UV) version of Multi-Filter Rotating Shadowband Radiom-eter (UV-MFRSR) to measure direct, diffuse, and total irradiances every 3 minutes at 7 UV channels (i.e. 300, 305, 311, 317, 325, 332, and 368 nm channels with 2 nm full width at half maximum). There have been plenty of literature exploring retrieval methods of total column ozone from UV-MFRSR measurements, but few has explored the retrieval of surface ozone. Under clear-sky conditions, UV irradiances absorption by ozone are significant and variable by height and wavelength. Therefore, multi-channel UV irradiances at the ground have the potential to resolve ozone concentrations at multiple vertical layers (including surface ozone). In this study, we used a deep learning algorithm (i.e. Self-Normalizing Neural Network, SNN) to retrieve surface ozone from 3-minute UV-MFRSR direct and diffuse irradiances (and the airmass) under clear-sky conditions at the UVMRP station located at Billings, Oklahoma. The 3-minute surface ozone data for training and validation are accumulated from 1-second surface ozone measured at the collocated Southern Great Plains (SGP) station by US Department of Energy Atmospheric Radiation Measurement Climate Research Facility (ARM). To cover the cloudy conditions, we also explored several spatial interpolation techniques [i.e. Triangulation-based linear interpolation, Graph Convolutional Neural Network (GCNN or ChebNet), mixture model network (MoNet), and Re-current Neural Network (RNN)] to estimate the hourly surface ozone at the same UVMRP station from the adjacent (i.e. within the 3-degree box of) US Environmental Protection Agency (EPA) hourly surface ozone observations.
Usage of Shape From Focus Method For 3D Shape Recovery And Identification of ...CSCJournals
Shape from focus is a method of 3D shape and depth estimation of an object from a sequence of pictures with changing focus settings. In this paper we propose a novel method of shape recovery, which was originally created for shape and position identification of glass pipette in medical hybrid robot. In proposed algorithm, Sum of Modified Laplacian is used as a focus operator. Each step of the algorithm is tested in order to pick the operators with the best results. Reconstruction allows not only to determine shape but also precisely define position of the object. The results of proposed method, performed on real objects, have shown the efficiency of this scheme.
While Phosphorous (31P) MRS (I) has been promising in experimental and clinical settings since the early 70s, it has been beset by prohibitively lower sensitivity, limited spectral-spatial resolution, and prolonged acquisition. This manuscript and proceedings of the annual scientific meeting of ISMRM in 2022 (REF1) and 2023 (REF2) demonstrate that our novel acquisition strategy, the novel Rosette Trajectory for fast and flexible MR(S)I contrast (Shen et al. 2023 (REF3), later we renamed it as PETALUTE after the translation to the preclinical scanners of 7T and 9.4T), enables operator-independent (1) rapid acquisition (~7 minutes), (2) reconstruction, and (3) processing pipeline, resulting in phosphorous metabolite ratio maps (10 x 10 x 10 mm3) of the whole brain.
In response to the “Repeat it with Me” challenge organized by the Reproducible Research study group of ISMRM, we demonstrated the power of this technique in 5 healthy volunteers at three different institutions with different experimental setups (2nd Place: UTE 31P 3D Rosette MRSI Reproducibility Team, REF4). Since the proposed acquisition/reconstruction/processing pipeline was operator/scanner/coil-independent, the Reproducer sub-teams successfully replicated the findings of the original proceeding in 2022 (REF1). As part of this challenge, we provided some MATLAB scripts and k-space data to reproduce some of the results described in this manuscript. The software and data can be downloaded from https://purr.purdue.edu/projects/ismrm31pmrsi.
These results will likely be of broad interest across clinical settings since the proposed acquisition strategy is not specific to any region, nuclei, or magnetic field and is operator-independent. This study's resolution and signal-to-noise ratios permit the metabolite maps in an experimentally and clinically feasible timeframe at 3 Tesla and 7T.
REF1 Bozymski B, Shen X, Ozen AC, Ibey S, Chiew M, Thomas A, Dydak U, Emir UE. Ultra-Short Echo Time 31P 3D MRSI at 3T with Novel Rosette k-space Trajectory. Proceedings 30th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2022.
REF2 Farley N, Bozymski B, Dydak U, Emir UE*. Fast 3D 31P MRSI Using Novel Rosette Petal Trajectory at 3T with x4 Accelerated Compressed Sensing. Proceedings 31st Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2023.
REF3 Shen X, Özen AC, Sunjar A, Ilbey S, Sawiak S, Shi R, Chiew M, Emir UE. Ultrashort T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magnetic Resonance in Medicine. 2023;89(2):508–521.
REF4 https://challenge.ismrm.org/2023-24-reproducibility-challenge/results-22-23/
Sensitivity of Support Vector Machine Classification to Various Training Feat...Nooria Sukmaningtyas
Remote sensing image classification is one of the most important techniques in image
interpretation, which can be used for environmental monitoring, evaluation and prediction. Many algorithms
have been developed for image classification in the literature. Support vector machine (SVM) is a kind of
supervised classification that has been widely used recently. The classification accuracy produced by SVM
may show variation depending on the choice of training features. In this paper, SVM was used for land
cover classification using Quickbird images. Spectral and textural features were extracted for the
classification and the results were analyzed thoroughly. Results showed that the number of features
employed in SVM was not the more the better. Different features are suitable for different type of land
cover extraction. This study verifies the effectiveness and robustness of SVM in the classification of high
spatial resolution remote sensing images.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
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When stars align: studies in data quality, knowledge graphs, and machine lear...
IGARSS_Presentation_Rodrigo_Jose_Pisani.ppt
1. Is it possible to make pixel-based radar image classification user friendly? IEEE International Geoscience and Remote Sensing Symposium - IGARSS Rodrigo José Pisani 1 , Alessandra Rodrigues Gomes 2 Paulina Setti Riedel 1 , Rodrigo Mizobe 3 , João Paulo Papa 3 1 UNESP – Geosciences and Exact Sciences Institute – Rio Claro, Brasil 2 INPE – National Institute for Space Research – Belém, Brasil 3 UNESP – Department of Computing – Bauru, Brasil Vancouver/Canada – July/2011
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3. In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets for the land use context; As the amount of data to be processed and further classified has increased in the last years; Several techniques have been designed to adapt the well succeed Support Vector Machines (SVMs) for large datasets. Recently, the Optimum-Path Forest (OPF) was proposed in order to provide graph-based pattern recognition algorithms with reduced training time (Papa et al., 2009). Introduction
4. Optimum-Path Forest Given a training set with samples from distinct classes, we wish to design a pattern classifier which can assign the true class label to any new sample (supervised classification); Each sample is represented by a set of features and a distance function measures their dissimilarity; The training samples are then interpreted as the nodes of a graph, whose arcs are defined by a given adjacency relation and weighted by the distance function.
5. Optimum paths are computed from the prototypes to each training sample, such that each prototype becomes a root of an optimum-path tree composed by its most strongly connected samples; Figure 1a : optimum-path forest generated over a training set.
6. In Figure 1b, one can see the classification process from a sample p (yellow node) of the evaluating set: p is connected to all nodes of the training set, and it is evaluated the training node t that offered the optimum-path in Figure 1c . Figure 1b: classification of a sample p from the evaluating set. Figure 1c: p is conquered by t and receives the red label.
7. Pruning training samples Papa et al. (2009b) proposed an algorithm that prunes the training set by identifying irrelevant samples in a learning process and further removing them; This approach has demonstrated to be very efficient and effective in some applications, speeding up the OPF training and classification times.
8. Figure 1d: all nodes from the optimum-path used to conquer p are marked with the black label. In Figure 1d, all training nodes that participate from the classification process of p are marked with the black color. At the final of the whole classification step, the unmarked training samples are discarded from the training set.
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10. Figure 2. Area of study – city of Duque de Caxias, RJ State.
11. Figure 3. ALOS PALSAR images used in the experiments covering the area of Duque de Caxias, RJ State. HH Polarization HV Polarization Each sample (pixel) is represented by HH and HV bands and by texture information given by a Gabor filter.
12. Slant to ground range Image Orthoretified Adaptative filter Lee 5 x 5 applied Image classification by OPF, SVM, BC and ANN-MLP Image rotulation (ground truth) Change the image to sigma 0 Quantitative and qualitative comparison (Kappa values) ALOS PALSAR IMAGE PROCESSING STEPS Original Alos Palsar Image (HH and HV) (without processing)
13. (Figures 3) there are four gray levels, which denote the following classes (ground truth): Difuse reflexion level II – (grass lands with average moist level) Volumeric backscattering (swampy and vegetation with very moist levels) Difuse reflexion level I – (grass lands with few moist level) Specular reflexion (plan areas with bare soil and dry areas) ALOS Palsar rotulated image
14. ALOS-PALSAR images: (a) original, (b) ground truth, (c) classified by OPF and (d) classified SVM. a b c Qualitative Results d
15. Table 1. Mean accuracy and execution times for Alos Palsar. Table 2. Quantitative results for pruning classification. Quantitative Results Classifier Kappa Mean Training time [s] Mean test time [s] OPF 0.71 15488.03 10122.24 BC 0.67 7698.76 22221.44 SVM Torch 0.75 176503.69 19379.61 ANN-MLP 0.38 43464.58 0.088919 Classifier Kappa Mean Training time [s] Mean test time [s] OPF 0.71 15488.03 10122.24 OPF-pruning 0.63 45498.76 564.26
16. The experiments showed good recognition rates, which enabled the image classification with interesting visual results; We demonstrated that we can speed up the classification time, but the accuracy may be degraded up to a certain level, mainly because of some training samples pruning; 4. Conclusions Nowadays, we are looking to develop soft strategies for pruning, which may less affect the accuracy, but may be also slightly slower than the implementation used here, but still faster than not using pruning .