ID: 56227
Title: Segmentation and thematic classification of color orthophotos over non-compressed and JPEG 2000 compressed images
Author: A Zabala, C Cea, X Pons
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Wavelets, Lossy compression effects, classification accuracy, quality assessment, emergency management
Abstract: Lossy compression is now increasingly used due to the enormous amount of images gathered by airborne and satellite sensors. Nevertheless, the implications of these compression procedures have been scarcely assessed. Segmentation before digital image classification is also a technique increasingly used in GEOBIA (GEOgraphic Object-Based Image Analysis). This paper presents an object-oriented application for image analysis using color orthophotos (RGB bands) and a Quickbird image (RGB and a near infrared band). We use different compression levels in order to study the effects of the data loss on the segmentation -based classification results. A set of 4 color orthophotos with 1m spatial resolution and a 4-band Quickbird satellite image with 0.7 m spatial resolution each covering an area of about 1200 x 1200 m2 (144 ha) was chosen for the experiment. Those scenes were compressed at 8 compression ratios (between 5:1 and 1000:1) using the JPEG 2000 standard.
There were 7 thematic categories: dense vegetation, herbaceous, bare lands, road and asphalt areas, building areas, swimming pools and rivers (if necessary). The best category classification was obtained using a hierarchical classification algorithm over the second segmentation level. The same segmentation and classification methods were applied in order to establish a semi-automatic technique for all 40 images.
To estimate the overall accuracy, a confusion matrix was calcuated using a photointerpreted groundtruth map (fully covering 25% of each orthophoto). The mean accuracy over non-compressed images was 66% for the orthophotos and 72% for the Quickbird iamge. It is interesting to obtain this medium overall accuracy to be able to properly assess the compression effects (if the initial overall accuracy is very high, the possible positive effects of compression would not be noticeable). The first and second compression levels (up to 10:1) obtain results similar to the reference ones. Differences in the third to fifth levels (20:1 to 100:1) were moderate to large (accuracies 61-58% for orthophotos and 67-65% for Quickbird), while more compressed images obtained the worst results (accuracies lower than 55%). As a comparison, the usual independent test areas (covering a small percentage of the classified area) were also used. The results show that this classification evaluation approach msut be used with caution because it may underestimate the classification errors.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56226
Title: Super-resolution mapping of lakes from imagery with a coarse spatial and fine temporal resolution
Author: Anuar M Muad, Giles M Foody
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Resolution enhancement, mixed pixel, halftoning, 2D multiple notch filter, object based morphology
Abstract: The potential of super-resolution mapping (SRM) techniques for the representation of lakes was evaluated using both an established and a newly proposed method. Both super-resolution mapping techniques were typically able to provide representations that were visually and quantitatively more realistic than standard hard classifications. The new technique was able to represent more small lakes than the established technique. The results also demonstrate the value of using a time series of images as input to the super-resolution anlaysis, enablisng researchers to usefully exploit the typically fine temporal resolution of coarse spatial resolution sensors for land cover mapping.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56225
Title: A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery
Author: A S Laliberte, D M Browning, A Rango
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Feature selection, Object based image analysis, Aerial imagery, very high resolution, classification
Abstract: The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature selection methods have been used in conjuction with OBIA, a robust comparison of the utility and efficiency of approaches would facilitate broader and more effective implementation. In this study we evaluated three feature selection methods, (1) Jeffreys-Matusita distance (JM), (2) classification tree analysis (CTA), and (3) feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and /or reduce input features, and classification accuracies. For the five sites tested, JM resulted in the highest overall classification accuracies for three sites, while CTA yielded highest accuracies for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the OBIA software used in this analysis (eCognition). However, the feature ranking in FSO is not transparent and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. We explore the limitations, assumptions, and appropriate uses for this and other datasets.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56224
Title: Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images
Author: Juan P Ardila, Wietske Bijker, Valentyn A Tolpekin, Alfred Stein
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: GEOBIA, Object based iamge analysis, Tree mapping, Tree crown identification, context classification, object classification
Abstract: Municipalities need accurate and updated inventories of urban vegetation in order to manage green resources and estimate their return on investment in urban forestry activities. Earlier studies have shown that semi-automatic tree detection using remote sensing is a challenging task. This study aims to develop a reproducible geographic object-based image anaysis (GEOBIA) methodology to locate and delineate tree crowns in urban areas using high resolution imagery. We propose a GEOBIA approach that considers the spectral, spatial and contextual characteristics of tree objects in the urban space. The study presents classification rules that exploit object features at multiple segmentation scales modifying the labeling and shape of image-objects. The GEOBIA methodology was implemented on QuickBird images acquired over the cities of Enschede and Delft (The Netherlands), resulting in an identification rate of 70% and 82% respectively. False negative errors concentrated on small trees and false positive errors in private gardens. The quality of crown boundaries was acceptable, with an overall delineation error <0.24 outside of gardens and backyards.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56223
Title: Frequency distribution signatures and classification of within-object pixels
Author: Douglas A Stow, Sory I Toure, Christopher D Lippitt, Caitlin L Lippitt, Chung-rui Lee
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Object classification, curve matching, Quicbird, Land cover, land use, Ghana
Abstract: The premise of geographic object-based image analysis (GEOBIA) is that image objects are composed of aggregates of pixels that correspond to earth surface features of interest. Most commonly, image-derived objects (segments) or objects associated wth predefined land units (e.g., agricultural fields) are classified using parametric statistical characteristics (e.g, mean and standard deviation) of the within-object pixels. The objective of this exploratory study was to examine the between- and within-class variability of frequency distributions of multispectral pixel values, and to evaluate and a quantitative measure and classification rule that exploits the full pixel frequency distribution of within-object pixels (ie.,histogram signatures) compared to simple parametric statistical characteristics. High spatial resolution QuickBird satellite multispectral data fo Accra, Ghana were evaluated in the context of mapping land cover and land use and socioeconomic status. Results show that image objects associated with land cover and land use types can have characteristic, non-normal frequency distributions (histograms). Signatures of most image objects tended to match closely the training signature of a single class or sub-class. Curve matching approaches to classifying multi-pixel frequency distributions were found to be slightly more effective than standard statistical classifiers based on a nearest neighbor classifier.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56222
Title: Monitoring urban changes based on scale-space filtering and object-oriented classification
Author: G Doxani, K Karantzalos, M Tsakiri-Strati
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Object-based image analysis, multivariate alteration detection, morphological scale space filtering
Abstract: This paper introduces a multi-temporal image processing framwork towards an efficient and (semi-) automated detection of urban changes. Nonlinear scale space filtering was embedded in an object-based classification procedure and the resulted simplified images provided a more compact and reliable source in order to generate image objects in various scales. In this manner the multiresolution segmentation outcome was constrained qualitatively. Multivariate alteration detection (MAD) transformation was applied afterwards on the simplified data to facilitate the detection of possible changes. The altered image regions along with the simplified data were further analyzed through a multilevel knowledge - based classification scheme. The developed algorithm was implemented on a number of multi-temporal data acquired by differetn remote sensing sensors. The qualitative and quantitative evaluation of change detection results performed with the help of the appropriate ancillary ground truth data. Experimental results demonstrated the effectivenss of the developed scale-space, object-oriented classification framework.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56221
Title: A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada
Author: Gang Chen, Geoffrey J Hay, Benoit St-Onge
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: GEOBIA, Geo-intelligence, Forest parameters, Lidar transects, Quickbird, Machine learning
Abstract: The GEOgraphic Object-Based Image Analysis (GEOBIA) paradigm continues to prove its efficacy in remote sensing image analysis by providing tools which emulate human perception and combine analyst ' s experience with meaningul image-objects. However, challneges remain in the evolution of this new paradigm as sophisticated methods attempt to deliver on the goal of automated geo-intelligence (i.e., geospatial content within context) from geospatial sources. In order to generate geo-intelligence from a forest scene, this article introduces a GEOBIA framework to estimate canopy height, above-ground biomass (AGB) and volume by combining lidar (light detection and ranging) transects, Quickbird imagery and machine learning algorithms. This framwork is comprised three main components: (i) image-object extraction, (ii) lidar transect selection, and (iii) forest parameter generalization. The rational for integrating these methods is to provide a semi-automatic GEOBIA approach from which detailed forest information is obtained at the individual tree crown or small tree cluster level (i.e., mean object size of 0.04 ha); while also dramatically reducing airborne lidar data acquisition costs. Analysis is performed over a 16,330 ha forested study site in Quebec, Canada. Forest parameter estimation results derived from our GEOBIA framework demonstrate a strong relationship with those using the full lidar cover; where the highest estimates for canopy height (R=0.85; RMSE =3.37 m), AGB (R = 0.85; RMSE=39.48 Mg/ha) and volume (R = 0.85; RMSE = 52.59 m3/ha) were achieved using a lidar transect sample representing only 7.6% of the total study area.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56220
Title: Quantitative land cover change analysis using fuzzy segmentation
Author: Ivan Lizarazo
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Fuzzy image segmentation, continuous land cover, change analysis, impervious surface mapping
Abstract: Fuzzy image segmentation was proposed recently as an alternative GEOBIA method for conducting discrete land cover classification. In this apper, a variant of fuzzy segmentation is applied for continuous land cover change analysis. The method comprises two main stages : (i) estimation of compositional land cover for each data by fuzzy segementation ; and (ii) change analysis using a fuzzy change matrix. The fuzzy segementation stage outputs fuzzy-crisp and crisp-fuzzy image regions whose spectral and geometric properteis are measured to populate the set of predictors used to estimate land cover at single dates. The variant of fuzzy image segmentation is implemented using advanced machine learning techniques and tested in a rapidly urbanizing area using Landsat multi-spectral imagery. Experimental results suggest that the method produces accurate characterization of continuous land cover classes. Thus, the proposed method is potentially useful for enhancing the current GEOBIA perspective which focuses mainly on discrete land cover classification.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56219
Title: Accuracy assessment of contextual classification results for vegetation mapping
Author: Guy Thoonen, Koen Hufkens, Jeroen Vanden Borre, Toon Spanhove, Paul Scheunders
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Contextual classification, Natura 2000, Accuracy assessment, confusion matrix
Abstract: A new procedure for quantitatively assessing the geometric accuracy of thematic maps, obtained from classifying hyperspectral remote sensing data, is presented. More specifically, the methodology is aimed at the comparison between results from any of the currently popular contextual classification strategies. The proposed procedure characterises the shapes of all objects in a classified image by defining an appropriate reference and a new quality measure. The results from the proposed procedure are represented in an intuitive way, by means of an error matrix, analogous to the confusion matrix used in traditional the - matic accuracy representation. A suitable appliation for the methodology is vegetation mapping, where lots of closely related and spatially connected land cover types are to be distinguishes. Consequently, the procedure is tested on a heathland vegetation mapping problem, related to Natura habitat monitoring. Object-based mapping and Markov Random Field classification results are compared, showing that the selected Markov Random Fields approach is more suitable for the fine-scale problem at hand, which is confirmed by the proposed procedure.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56218
Title: Introduction to the GEOBIA 2010 special issue: From pixels to geographic objects in remote sensing image analysis
Author: Editorial
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 15, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: GEOBIA, segmentation, spatial domain
Abstract: Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye-brain combination does. The latter uses the object ' s color (spectral information), size, texture, shape and occurence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object ' s properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56217
Title: A review on functionalized ionic liquids - based on Benzimidazolium cation: solvents for synthesis and catalysis
Author: Muskawar Prashnat Narayan, Sythana Suresh Kumar, Aswar Sachin Arunrao, Parasuraman Karthikeyan and Bhagat PundlikRambhau
Editor: Dr Shankar Gargh
Year: 2011
Publisher: Research Journal of Chemistry and Environment, Vol 15(4), December 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Research Journal of Chemistry and Environment
Keywords: Functionlized ionic liquids, benzimidazolium cation, solvents, metal-medicated catalyst, solar cell, organic reactions
Abstract: Functionalized ionic liquids (ILs) based on Benzimidazolium cation as environmental friendly solvent and catalyst, high activity and selectivity and easily recovered materials were used to replace traditional volatile organic solvents which generally suffered from disadvantages such as waste products, corrosion and environmental problems. Benzimidazole based ILS offered a new possibility for developing environmentally friendly basic catalyst with transition metal (Ag, Pd). They are flexible, nonvolatile, noncorrosive and immiscible with many organic solvents. This review presents an overview of the preparation, extraction and applications in various reactions like Alkylation, Esterification, Acetalization, Benzoin reaction and Metal mediated catalyst in Suzuki, Suzuki Miyaura, Heak cross coupling and reduction, also in Anion Sensor, Solar Cell, Proton Conduction, Malaria Parasite and Biodiesel.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56216
Title: Eco-friendly supported nanoparticles as a green approach
Author: Sadegh Rostamnia
Editor: Dr Shankar Gargh
Year: 2011
Publisher: Research Journal of Chemistry and Environment, Vol 15(4), December 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Research Journal of Chemistry and Environment
Keywords: Eco-friendly, nanoparticles, quantum dot, green approach
Abstract: Nanoparticles based on toxic properties of quantum dot (Q.D) have some limitation in environment and green applications. In view of the great attention paid to the development of eco-friendly and green chemistry approaches and based on recent advances in green support media, eco-friendly supported nanoparticles are used as catalyst that have led to a rational approach to the design of new heterogeneous green catalysts. Amongst the advantages of supported nanoparticles, the most significant is their large size (no. Q.D) and hence they can accommodate a greateer number of eco-friendly surfaces such as clays, zeolites etc. for better catalytic activity and green media under controlled conditions.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56215
Title: Assessment of toxic effects of chloramphenicol in rats and its amelioration by coconut water
Author: Dubey Chetan, Saxena Abhilasha, Gupta Rakhi, Singh Poonam, Bansode F W and Singh R K
Editor: Dr Shankar Gargh
Year: 2011
Publisher: Research Journal of Chemistry and Environment, Vol 15(4), December 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Research Journal of Chemistry and Environment
Keywords: Hepatotoxicity, chloramphenicol, coconut water, haematotoxicity
Abstract: Chloramphenicol was administered at dose levels of 150 mg/kg in rats for 14 days which caused significant haematatoxicity and hepatotoxicity. Haematotoxicity and hepatotoxicity were reversed with the use of coconut water (20 ml/kg) in 14 days establishing that coconut water has potential for amelioration of chloramphenicol toxicity. Glucuronidation of chloramphenicol followed by its elimination from the body is done by vitamins of group B (flavins) present in coconut water on stimulation of metabolism of drug. Abundance of its aqueous enhances the dilution effect which is responsible for rapid increase in degree of absorption of chemicals in gastrointestinal tract.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 56214
Title: A study of age related decrease in zinc and chromium and its correlations with type 2 diabetes mellitus
Author: Singh Ruchi and Kumar Ashok
Editor: Dr Shankar Gargh
Year: 2011
Publisher: Research Journal of Chemistry and Environment, Vol 15(4), December 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Research Journal of Chemistry and Environment
Keywords: zinc, chromium, AAS, fasting, postprandial, type 2 diabetes, age
Abstract: The aime of this study was to compare the levels of zinc and chromium in serum samples (fasting and postprandial) of type 2 diabetic (NIDDM) subjects age ranged (30-75: n = 56) with those of age matched non diabetics as normals (n = 40) of both genders and further access a correlationship on the basis of age. The concentration was measured by means of an Atomic Absorption Spectrophotometer after wet acid digestion of serum samples. The results of this study showed significantly lower level (p<0.001) both for zinc and chromium in fasting and postprandial samples of diabetics than normals. Age wise decline in zinc and chromium status in human serum was also observed. These results are in consistence with those of earlier studies confirming that trace metals may play a role in the development of NIDDM subjects.
Location: 241
Literature cited 1: None
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ID: 56213
Title: Quantifying spatial variability of peat soil carbon and nitrogen using infrared spectroscopy, statistical and geo-statistical models
Author: Aslan Guler, Fatih Evrendilek and Nusret Karakaya
Editor: Dr Shankar Gargh
Year: 2011
Publisher: Research Journal of Chemistry and Environment, Vol 15(4), December 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Research Journal of Chemistry and Environment
Keywords: Biogeochemical cycles, environmental monitoring, peatland, soil quality, PLS, IR, ATR-FTIR
Abstract: Chemical and biological analyses of soil carbon (C) and nitrogen (N) are time-consuming or require fresh material in cases of intensive in situ sampling. Infrared spectroscopy is one of the rapid and non-destructive methods that can be applied to a large number of soil smaples. In this study, Attenuated Total Reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy in the range of 600 to 4000 cm-1was assessed using partial least squares (PLS) regression model to predict total C and N of peat soils. ATR/FTIR-based PLS models had r2 values of about 0.8 for fitted functions and 0.7 for leave-one-out cross-validations. Using an independent dataset to compare soil C and N values estimated by ATR/FTIR-based PLS models versus those mesrued using CHN elemental analyzer led to r2 value of 0.97 for both soil C and N. The combined use of ATR/FTIR-based PLS models and inverse distance weighting (IDW) interpolation appears to be a promising methos to estimate total C and N of peat soils for rapid data acquisition across spatially extensive areas.
Location: 241
Literature cited 1: None
Literature cited 2: None