Top PDF CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR) SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR) SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

CLASSIFIER FUSION OF HIGH-RESOLUTION OPTICAL AND SYNTHETIC APERTURE RADAR (SAR) SATELLITE IMAGERY FOR CLASSIFICATION IN URBAN AREA

These days, many remote sensing satellite sensors are acquiring information at different spatial, spectral and temporal resolutions. However, the information provided by the individual sensors might be incomplete or imprecise for a given application (Hall and Llinas, 1997). Combing microwave and optical sensors can help in discriminating the different classes since they are complementary to each other (Pohl and Van Genderen, 1998). Many studies have combined optical image and microwave image to improve mapping accuracy in different scenarios. SAR and optical imagery can be integrated in different approaches to improve the data and information content during image processing for information extraction. Classifier fusion is a technique which can combine the optical and SAR sensor data, at decision level. The purpose of radar and optical fusion is mainly to use synergy between SAR and optical images for increasing discriminant power between different classes (van der Sanden and Thomas, 2004; Schist ad- Solberg et al., 1994).
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UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS

UNSUPERVISED WISHART CLASSFICATION OF WETLANDS IN NEWFOUNDLAND, CANADA USING POLSAR DATA BASED ON FISHER LINEAR DISCRIMINANT ANALYSIS

Polarimetric Synthetic Aperture Radar (PolSAR) imagery is a complex multi-dimensional dataset, which is an important source of information for various natural resources and environmental classification and monitoring applications. PolSAR imagery produces valuable information by observing scattering mechanisms from different natural and man-made objects. Land cover mapping using PolSAR data classification is one of the most important applications of SAR remote sensing earth observations, which have gained increasing attention in the recent years. However, one of the most challenging aspects of classification is selecting features with maximum discrimination capability. To address this challenge, a statistical approach based on the Fisher Linear Discriminant Analysis (FLDA) and the incorporation of physical interpretation of PolSAR data into classification is proposed in this paper. After pre-processing of PolSAR data, including the speckle reduction, the H/α classification is used in order to classify the basic scattering mechanisms. Then, a new method for feature weighting, based on the fusion of FLDA and physical interpretation, is implemented. This method proves to increase the classification accuracy as well as increasing between-class discrimination in the final Wishart classification. The proposed method was applied to a full polarimetric C-band RADARSAT-2 data set from Avalon area, Newfoundland and Labrador, Canada. This imagery has been acquired in June 2015, and covers various types of wetlands including bogs, fens, marshes and shallow water. The results were compared with the standard Wishart classification, and an improvement of about 20% was achieved in the overall accuracy. This method provides an opportunity for operational wetland classification in northern latitude with high accuracy using only SAR polarimetric data.
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Forest and Forest Change Mapping with C- and L-band SAR in Liwale, Tanzania

Forest and Forest Change Mapping with C- and L-band SAR in Liwale, Tanzania

As part of a Tanzanian-Norwegian cooperation project on Monitoring Reporting and Verification (MRV) for REDD+, 2007-2011 C- and L-band synthetic aperture radar (SAR) backscatter data from Envisat ASAR and ALOS Palsar, respectively, have been processed, analysed and used for forest and forest change mapping over a study side in Liwale District in Lindi Region, Tanzania. Land cover observations from forest inventory plots of the National Forestry Resources Monitoring and Assessment (NAFORMA) project have been used for training Gaussian Mixture Models and k-means classifier that have been combined in order to map the study region into forest, woodland and non-forest areas. Maximum forest and woodland extension masks have been extracted by classifying maximum backscatter mosaics in HH and HV polarizations from the 2007-2011 ALOS Palsar coverage and could be used to map efficiently inter-annual forest change by filtering out changes in non-forest areas. Envisat ASAR APS (alternate polarization mode) have also been analysed with the aim to improve the forest/woodland/non-forest classification based on ALOS Palsar. Clearly, the combination of C-band SAR and L-band SAR provides useful information in order to smooth the classification and especially increase the woodland class, but an overall improvement for the wall-to-wall land type classification has yet to be confirmed. The quality assessment and validation of the results is done with very high resolution optical data from WorldView, Ikonos and RapidEye, and NAFORMA field observations.
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APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA

APPLICATION OF PALSAR-2 REMOTE SENSING DATA FOR LANDSLIDE HAZARD MAPPING IN KELANTAN RIVER BASIN, PENINSULAR MALAYSIA

The Advanced Land Observing Satellite-2 (ALOS-2) was launched on May 24, 2014 as successor of ALOS-1 (launched on January 24, 2006 and decommissioned in May 2011). The ALOS-2 is exclusively installed with the Phased Array type L- band Synthetic Aperture Radar-2 (PALSAR-2) using microwaves to maximized its ability compare to the ALOS-1, on which three sensors (two optical and one microwave devices) were onboard (Suzuki et al., 2012). PALSAR-2 of the ALOS-2 has been significantly improved from the ALOS-1’s PALSAR in all aspects, including resolution, observation band and time lag for data provision (Suzuki et al., 2012). ALOS-2 science capabilities include global environmental monitoring using the time-series PALSAR-2. The research target also covers biospheric, cryospheric and coastal ocean research as well as disaster mitigation (Shimada, 2013). PALSAR-2 is a microwave sensor that emits L-band radio waves and receives their reflection from the ground to acquire information (Suzuki et al., 2012). It has three observation modes, including (i) Spotlight mode: the most detailed observation mode with 1 by 3 meters resolution and observation width of 25 km; (ii) Strip map mode: a high-resolution mode with the choice of 3 (ultra fine), 6 (high sensitivity) or 10 (fine) meters resolution and observation width of 50 or 70 km; and (iii) ScanSAR mode: a broad area observation width of 350 (nominal) or 490 (wide)
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Measurement of turbulence in the oceanic mixed layer using Synthetic Aperture Radar (SAR)

Measurement of turbulence in the oceanic mixed layer using Synthetic Aperture Radar (SAR)

with the background NRCS: such details are more visible when the radar look is aligned in a direction with sufficient presence of short waves, e.g. Figure 10d. In the real ocean, the directionality of short wind waves is likely to be much broader than in this simple case of a uniform wind direction due to spatial variation of both wind speed and direc- tion. Therefore, these results present a “worst-case” state of conditions pertaining to

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WATERLINE DETECTION AND MONITORING IN THE GERMAN WADDEN SEA USING HIGH RESOLUTION SATELLITE-BASED RADAR MEASUREMENTS

WATERLINE DETECTION AND MONITORING IN THE GERMAN WADDEN SEA USING HIGH RESOLUTION SATELLITE-BASED RADAR MEASUREMENTS

small-scale structures like tidal inlets. Tidal mudflats, on the other hand, consist of a mix of highly reflective dry sand, mud and shallow water puddles acting as a specular reflector yield- ing almost no reflectivity. Hence, the NRCS of these surfaces strongly varies from very high levels to very low levels (van der Wal et al., 2005). The NRCS of the surrounding seawater is deter- mined by wind field conditions (Li and Lehner, 2014) and often higher than mudflat areas. As a consequence, algorithms using scene brightness as primary criterion (e.g. Acar et al., 2012; Liu and Jezek, 2004) will work well in high tide, but are hardly appli- cable to low tide Wadden Sea scenes. Algorithms like Dellepiane et al. (2004); Yu and Acton (2004); Gade et al. (2014) use in- terferometric, polarimetric or multi-band SAR data; however, we
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An object based approach for coastline extraction from Quickbird multispectral images

An object based approach for coastline extraction from Quickbird multispectral images

The output of each fuzzy rule is a confidence map, where values represent the membership degree to the feature type defined by this rule. In classification, the object is assigned to the feature type that has the maximum confidence value. Two classes were considered for the classification: a class for water (WATER) and a class for everything that was not water (NO WATER). The rules definition was based on the estimation of thresholds for considered classes. These thresholds were derived from a careful analysis of the histograms of NDVI (Normalized Difference Vegetation Index). NDVI is function that varies in the range [-1, +1]. Negative values correspond to water, values close to zero but positive values correspond to soils and further, from 0.2 indicate the presence of surfaces vegetated with maximum values around 0.8 for very dense vegetation [17]. NDVI was used to better define the coastline location and calculated using the expression [1]:
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PHOTOGRAMMETRY AND REMOTE SENSING: NEW GERMAN STANDARDS (DIN) SETTING QUALITY REQUIREMENTS OF PRODUCTS GENERATED BY DIGITAL CAMERAS, PAN-SHARPENING AND CLASSIFICATION

PHOTOGRAMMETRY AND REMOTE SENSING: NEW GERMAN STANDARDS (DIN) SETTING QUALITY REQUIREMENTS OF PRODUCTS GENERATED BY DIGITAL CAMERAS, PAN-SHARPENING AND CLASSIFICATION

In the past 20 years, a large effort has been taken to characterize the image quality of remote sensing systems. Such definitions are important for the evaluation of image products. The image quality can only be measured by the quality of the final product (e.g. after object detection, classification, etc.). One option was to use the National Image Interpretability Rating Scales (NIIRS) since NIIRS is related to object detection. From an engineering standpoint, a task-based scale, like NIIRS, is not well suited because it cannot be derived from the fundamental sensor and scene behaviour. Therefore, the aim of this standard is to derive an image quality criterion based on the physical characteristics of sensor and scene. One approach (Jahn, 2012) is to assess the image quality by comparing the output of the real sensor with the output of an ideal sensor based on a local mean squared error (LMSE).
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Rainfall observation from X-band, space-borne, synthetic aperture radar

Rainfall observation from X-band, space-borne, synthetic aperture radar

Abstract. Satellites carrying X-band Synthetic Aperture Radars (SAR) have recently been launched by several coun- tries. These provide new opportunities to measure precipita- tion with higher spatial resolution than has heretofore been possible. Two algorithms to retrieve precipitation from such measurements over land have been developed, and the re- trieved rainfall distributions were found to be consistent. A maritime rainfall distribution obtained from dual frequency (X and C-band) data was used to compute the Differential Polarized Phase Shift. The computed Differential Polarized Phase Shift compared well with the value measured from space. Finally, we show a comparison between a recent X- band SAR image of a precipitation distribution and an ob- servation of the same rainfall from ground-based operational weather radar. Although no quantitative comparison of re- trieved and conventional rainfall distributions could be made with the available data at this time, the results presented here point the way to such comparisons.
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Tomógrafo em nível de simulação utilizando micro-ondas em banda ultra larga (UWB)...

Tomógrafo em nível de simulação utilizando micro-ondas em banda ultra larga (UWB)...

As técnicas de geração de imagens médicas usando micro-ondas para detecção de câncer de mama podem ser divididas em duas classes principais: sistemas ativos e sistemas passivos. Os sistemas passivos são conhecidos comumente como radiometria de micro-ondas (Microwave radiometry), ou também como termográfica (Thermohraphy). Os sistemas ativos se dividem, por sua vez, em microscopia de micro-ondas , microwave tomography (MT), e métodos híbridos. Dentro destas técnicas microwave tomography (MT) baseado em radar de pulsos UWB tem demostrado uma boa resolução e baixo custo, o qual é um fator importante para o uso massivo deste tipo mamógrafos (ZHURBENKO, 2011) (BASSI, BEVILACQUA, et al., 2012).
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J. Aerosp. Technol. Manag.  vol.8 número2

J. Aerosp. Technol. Manag. vol.8 número2

In the recognition of micro-motion, the CLEAN algorithm (Caner 2012) can be used to extract the coordinate information of the peak value of the image to get the coordinate information of the point trace. he scattering intensity of the strong scattering source is not always strong under all conditions; under some radar observation angle, the scattering intensity of the cone node strong scattering source may be weak, such as the ISAR images in the second row of Fig. 3a. he scattering intensity of the cone node strong scattering source is weak. It is hard to extract cone node strong scattering source by the CLEAN algorithm, but the cone node strong scattering source is located in the cone node and can be manually extracted by the position of the pointed cone in the ISAR image’s cone contour. hen, the Doppler domain value of the strong scattering source on the imaging plane should be judged. If it is smaller than the set threshold, its corresponding micro-motion form is considered spinning. Otherwise, it is precession or nutation. hen a line segment set is established through connecting the strong scattering sources among adjacent ISAR image sequences; the number of cross points is counted by judging whether line segments intersect. But sometimes the projection trajectories of the different scattering sources overlap, as show in Fig. 8, which has a certain impact on the number of cross points statistics. In addition, single ISAR image exists in multiple scattering centers, so the projection of multiple scattering sources at the adjacent time needs to be related. herefore, it is necessary to separate the projected trajectory of each scattering source. In this paper, the projection trajectory of the cone target’s strong scattering source on the imaging plane is interpreted as the track of target tracking. he target tracking technique is applied to extract the projection trajectory of each scattering source in the ISAR image sequence.
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The application of optical measurements for the determination of accuracy of gear wheels casts manufactured in the RT/RP process

The application of optical measurements for the determination of accuracy of gear wheels casts manufactured in the RT/RP process

The article discusses the possibilities of using optical measurements for defining the geometric accuracy of gear wheels casts manufactured in the rapid prototyping process. The tested gear wheel prototype was cast using an aluminum alloy. The casting mould was made by means of the three-dimensional print method (3DP) with the use of a Z510 Spectrum device. The aim of the tests was to determine the geometric accuracy of the cast made by the ZCast technology in the rapid prototyping process. The tests were conducted with the use of the coordinate optical measuring method and a GOM measuring device. The prototype measurements were made in the scanning mode. The results of the measurements, saved in the STL format with the use of the scanning device software, were compared with the gear wheel 3D-CAD nominal model. The measurements enabled the determination of the real accuracy of prototypes manufactured in casting moulds by means of the ZCast technology. The selection of the measuring method was also analyzed in terms of measurement accuracy and the RP technology precision.
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Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery

Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery

Sub-meter satellite imagery can potentially provide an alternative to these field-based techniques in order to collect high spatial resolution topographic data over large areas. The first civil satellite constellation that acquired stereoscopic imagery and applied DEM reconstruction over large areas was the French SPOT mission (Satellite Pour l’Observation de la Terre) in 1986 [12]. Since then, several very high spatial resolution satellites with stereo capabilities were launched in response to an increased demand [13]. Among them, the Pleiades constellation (built by the French Space Agency (CNES), commercialized by AIRBUS Defence & Space), consists of two high spatial resolution optical spacecrafts: Pleiades –1A and –1B. Both satellites fly over the same near-polar sun-synchronous orbits at an altitude of 694 km with a 180 ◦ phase and descending node. The optical sensors of these satellites have the capability to obtain images with sub-meter image resolution (0.7 m pixel size, resampled to 0.5 m) over a maximum area of 350 km × 20 km (swath width of 20 km at nadir). An important aspect of Pleiades is the capacity to revisit any location in the world within 1 day, which is of great interest to monitor rapidly changing processes (e.g., coastal erosion due to storm events). Recent studies based on Pleiades-1A stereo-imagery include snow height mapping in mountainous areas [14], large landmass deformations due to earthquakes [15], surface reconstruction after landslides [16], and glacier topography [17,18].
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Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

GEOBIA approaches applied to current and future VHR satellite imagery may expedite the acquisition and updating of spatial information, an increasing requirement in the municipal context. To assess if this goal is feasible and in which conditions, it is fun- damental to develop and implement quality assessment methods which are rigorous, objective, and that take in consideration the technical constraints of the large mapping scales traditionally used at the local level. Such a method could also contribute to increase the confidence and guide the use of spatial data sets obtained through this process. The present work represents an innovative approach to assess the quality of buildings extracted from VHR sa- tellite imagery using semi-automated methods through analysis of similarity with a reference database, taking place in the context of large scale mapping to assist urban planning in Portugal. A new ap- proach was developed and demonstrated to adopt technical map- ping standards in an object-based evaluation of different spatial quality elements. Automatic feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. The approach evaluates different aspects that determine overall quality of a single feature class map, namely the- matic quality, completeness, and geometric quality. These dimen- sions were evaluated using consecutive object-based tests and quantitative quality metrics were produced. Quality assessment was exhaustive (i.e., by census) and involved comparisons of ex- tracted features against a reference data set, introducing carto- graphic constraints from large scales used at municipal level, namely 1:1000, 1:5000, and 1:10,000. Although the approach was illustrated for buildings with red tile roofs, it could be applied to different building types and even to other polygon-based geo- graphic features present in topographic maps that could be subject to automatic feature extraction from remotely-sensed imagery.
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Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

Analysis of vegetation dynamics using time-series vegetation index data from Earth Observation Satellites

The ability of PhenoSat to estimate phenological metrics from satellite VI data was evaluated by a compar- ison between PhenoSat derived phenology and field measures. Table 16.2 presents the statistics of field phenological measures obtained for each study area. For the VIN test site, phenological measures collected in the field according to the Baggiolini scale (Baggiolini 1952) were available. The bud break (BUB), flowering (FLO) and veraison (VER, define as the ‘change of color grapes’ stage) field observations were compared with the SOS, MAT and SEN derived by PhenoSat. As no ground measures of phenology were available for SNM, the PhenoSat results for SNM were compared with the reference measures (named field measures from this point) derived by visual inspection of the original VI time-series, taking into account the knowledge of the vegetation behavior in the field at normal conditions. As an example, figure 16.5 presents the field measures determined from the SNM for one year. The SOS was determined as the first point where a significant (four or more points) NDVI growth was occurred (March/April). The MVD was identified as the maximum NDVI value in the annual time-series, which generally occurs in June or early July. The abrupt decrease verified after this point is due to the grass cutting process. The remaining ground vegetation (about 5cm height) begins a senescence period until the maximum senescence (EOS), occurring mostly around August. In general the SNM EOS stage is followed by a regrowth (RG), representing the first significant (three or more points) vegetation growth after the EOS occurrence.
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Changes in the land cover and land use of the Itacaiunas River watershed, arc of deforestation, Carajas, southeastern Amazon

Changes in the land cover and land use of the Itacaiunas River watershed, arc of deforestation, Carajas, southeastern Amazon

3.2 Land cover and land use “from-to” change detection Figure 4 illustrates the LCLU unchanged and “from-to” change detection classes based on a bi-temporal mosaic image analysis. The change detection between 1984 and 1994 indicated that unchanged forest was the largest class with almost 2.8 millions ha (68% of the study area). The conversion from forest class in 1984 to pasture class in 1994 reached an area of 800,000 ha, while unchanged pasture encompassed ~300,000 ha (Table 2A). Between 1994 and 2004, the unchanged forest class represented around 2 millions ha, while unchanged pasture attained ~1 million ha. The conversion from forest to pasture in this period kept the same intensity of ~800,000 ha. The change detection between 2004 and 2013 is marked by an accentuated decrease in the LCLU changes, with unchanged forest and unchanged pasture occupying an area of approximately 1.8 and 1.7 million ha, respectively, which demonstrates that no change occurred in around ~85% of the study site. The conversion from forest to pasture was reduced to ~300,000 ha, while forest recovery from pasture reached the maximum intensity (~140,000 ha). Between 1984-2013, we could observe that ~47% (~1.9 million ha) of forest kept unchanged; almost 41% (~1.7 million ha) of changes was associated to conversion from forest to pasture, while 8% (~333,000 ha) remained unchanged pasture. The conversion of forest and montane savanna to mining area represents only 0.24% (~9,000 ha). The area and percentage of unchanged and “from-to” change detection between one class to another can be observed in the Table 2A.
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DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

LiDAR-derived DSM provides height information, however more structural features should be generated to improve its ability in discrimination between classes. The nDSM is generated from DSM by geodesic morphological operation. In order to analyse the nDSM accurately, several types of features such as texture analysis, roughness and slope descriptors are extracted. Grey Level Co-occurrence Matrices (GLCM) approach is used in this paper to extract second order statistical textural features from nDSM. In this paper, 16 features (Variance, Homogeneity, Contrast, Entropy, Dissimilarity, Sum Average, Angular Second Moment, Maximum Probability, Inverse Difference Moment, Sum Entropy, Sum Variance, Difference Variance, Correlation, Difference Entropy and two Information Measure of Correlation) are extracted from the GLCM matrix (Haralick et al. 1973). Roughness is another structural feature which is extracted from nDSM. For this purpose, the terrain roughness is parameterized by the standard deviation of the detrended z-coordinates of the neighborhood. The plane is fitted to each neighborhood by the least square method and then the standard deviation of detrended height is determined. Texture analysis on the roughness map is also performed to better analysis of roughness. Moreover the slope of each neighbourhood in the nDSM is computed by applying the normal vector for the obtained plane which leads to a contribution of the slope feature to the structural feature space. Finally, by stacking the spectral features from hyperspectral imagery and structural features from LiDAR data, the hybrid feature space is generated.
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Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood

Fusion of hyperspectral and lidar data based on dimension reduction and maximum likelihood

Limitations and deficiencies of different remote sensing sensors in extraction of different objects caused fusion of data from different sensors to become more widespread for improving classification results. Using a variety of data which are provided from different sensors, increase the spatial and the spectral accuracy. Lidar (Light Detection and Ranging) data fused together with hyperspectral images (HSI) provide rich data for classification of the surface objects. Lidar data representing high quality geometric information plays a key role for segmentation and classification of elevated features such as buildings and trees. On the other hand, hyperspectral data containing high spectral resolution would support high distinction between the objects having different spectral information such as soil, water, and grass. This paper presents a fusion methodology on Lidar and hyperspectral data for improving classification accuracy in urban areas. In first step, we applied feature extraction strategies on each data separately. In this step, texture features based on GLCM (Grey Level Co-occurrence Matrix) from Lidar data and PCA (Principal Component Analysis) and MNF (Minimum Noise Fraction) based dimension reduction methods for HSI are generated. In second step, a Maximum Likelihood (ML) based classification method is applied on each feature spaces. Finally, a fusion method is applied to fuse the results of classification. A co-registered hyperspectral and Lidar data from University of Houston was utilized to examine the result of the proposed method. This data contains nine classes: Building, Tree, Grass, Soil, Water, Road, Parking, Tennis Court and Running Track. Experimental investigation proves the improvement of classification accuracy to 88%.
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Landscape dynamics analysis in Iasi Metropolitan Area (Romania) using remote sensing data

Landscape dynamics analysis in Iasi Metropolitan Area (Romania) using remote sensing data

The highest explanatory value is offered by the last index, Area Weighted Mean Shape Index, which exactly express the degree of shape regularity. Apart from its general tendency to rise during the studied period, specific characteristics for each part of the Iasi Metropolitan Area should be stated. South-west area had the highest values and the sharpest rise, as it was , affected almost entirely by residential developments, which followed an irregular, dispersed pattern adapted to local topography, road networks and proximity to basic services. South-east and Nord-west parts also experienced a sharp increase, having been very dynamic both in large-scale developments and individual residential. The lower values of this indexes (compared to the south-west area) are due to the intake of compactness and geometric regularity brought by large-scale constructions (commercial units - in north-west and real-estate projects - in south- east). Finally, the north-east cell had the lowest values and the lowest rate of increase, due to its reduced surface and dynamics.
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Assessment Of NPK In Human Male And Female Urine For Its Fertilising Potential In Agriculture

Assessment Of NPK In Human Male And Female Urine For Its Fertilising Potential In Agriculture

The Statistical Package for Social Scientists (SPSS) software (version 15) was used in testing whether or not the means of dependent variables were significantly different among groups. The total % yield of nitrogen, phosphorus and potassium of the stored urine over the 6-month study period were analysed. This was indicative of when the urine could be used for crops that require proportionally high percentage of nitrogen, phosphorus or potassium. The significant difference in yield of NPK between male and female urines was also established for each month over the 6 months study period. If the overall ANOVA was significant and a factor had more than two levels, a post-hoc multiple comparisons follow up test was carried out using Least Significance Difference (LSD) or Duncan’s Multiple Range Test (DMRT). In all cases, significance was determined at the 95% confidence level. One-way analysis of variance was performed to assess the differences among means, with a significance level of 5% (p< 0.05).
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