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Hyperspectral Remote Sensing of Urban Areas: An Overview of Techniques and Applications  [cached]
Helmi Z.M. Shafri,Ebrahim Taherzadeh,Shattri Mansor,Ravshan Ashurov
Research Journal of Applied Sciences, Engineering and Technology , 2012,
Abstract: Over the past two decades, hyperspectral remote sensing from airborne and satellite systems has been used as a data source for numerous applications. Hyperspectral imaging is quickly moving into the mainstream of remote sensing and is being applied to remote sensing research studies. Hyperspectral remote sensing has great potential for analysing complex urban scenes. However, operational applications within urban environments are still limited, despite several studies that have explored the capabilities of hyperspectral data to map urban areas. In this paper, we review the methods for urban classification using hyperspectral remote sensing data and their applications. The general trends indicate that combined spatial-spectral and sensor fusion approaches are the most optimal for hyperspectral urban analysis. It is also clear that urban hyperspectral mapping is currently limited to airborne data, despite the availability of spaceborne hyperspectral systems. Possible future research directions are also discussed.
Monitoring temporary ponds dynamics in arid areas with remote sensing and spatial modelling
V. Soti,C. Puech,D. Lo Seen,A. Bertran
Hydrology and Earth System Sciences Discussions , 2010,
Abstract: A hydrologic pond model was developed that simulates daily spatial and temporal variations (area, volume and height) of temporary ponds around Barkedji, a village located in the Ferlo Region in Senegal. The model was tested with rainfall input data from a meteorological station and from Tropical Rainfall Measuring Mission (TRMM) satellites. During calibration phase, we used climatic, hydrologic and topographic field data of Barkedji pond collected daily during the 2002 rainy season. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and a QuickBird satellite image acquired in August 2005 (2.5 m pixel size) were used to apply the hydrologic model to all ponds (98 ponds) of the study area. With input rainfall data from the meteorological station, simulated water heights values for years 2001 and 2002 were significantly correlated with observed water heights for Furdu, Mous 2 and Mous 3 ponds, respectively with 0.81, 0.67 and 0.88 Nash coefficients. With rainfall data from TRMM satellite as model input, correlations were lower, particularly for year 2001. For year 2002, the results were acceptable with 0.61, 0.65 and 0.57 Nash coefficients for Barkedji, Furdu and Mous 3 ponds, respectively. To assess the accuracy of our model for simulating water areas, we used a pond map derived from Quickbird imagery (August 2007). The validation showed that modelled water areas were significantly correlated with observed pond surfaces (r2=0.90). Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data (Quickbird, ASTER DEM, TRMM) to assess pond water heights and water areas of a homogeneous arid area.
Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data  [PDF]
Murali Krishna Gumma,Prasad S. Thenkabail,Fujii Hideto,Andrew Nelson,Venkateswarlu Dheeravath,Dawuni Busia,Arnel Rala
Remote Sensing , 2011, DOI: 10.3390/rs3040816
Abstract: Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.
A Study of Morphological Changes in the Coastal Areas and Offshore Islands of Bangladesh Using Remote Sensing
American Journal of Geographic Information System , 2013, DOI: 10.5923/j.ajgis.20130201.03
Abstract: Bangladesh is a country where 80 percent of the land is below 1 meters from the MSL. The small country with a huge population is already known to the outside world for her vulnerability to natural hazards. Among the impacts of climate change, the serious concern for Bangladesh is the relative sea level rise (RSLR). A 45-centimeter sea level rise in Bangladesh may dislocate about 35 million people from 20 coastal districts by 2050. In a worst-case scenario, Bangladesh could lose nearly 25 percent of its 1989 land area by around 2100. The process of erosion, deposition and accretion are common phenomenon in the deltaic country. This study based on remote sensing data tries to examine the erosion, accretion and net gain/loss of land in the Coastal Areas and Offshore Islands of Bangladesh and recommends measures to enhance the process of accretion to save millions from becoming environmental refugees.
Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas  [cached]
Mathieu Fauvel,Jocelyn Chanussot,Jón Atli Benediktsson
EURASIP Journal on Advances in Signal Processing , 2009, DOI: 10.1155/2009/783194
Abstract: Kernel principal component analysis (KPCA) is investigated for feature extraction from hyperspectral remote sensing data. Features extracted using KPCA are classified using linear support vector machines. In one experiment, it is shown that kernel principal component features are more linearly separable than features extracted with conventional principal component analysis. In a second experiment, kernel principal components are used to construct the extended morphological profile (EMP). Classification results, in terms of accuracy, are improved in comparison to original approach which used conventional principal component analysis for constructing the EMP. Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data. For the one data set, the overall classification accuracy increases from 79% to 96% with the proposed approach.
Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery  [PDF]
Luis C. Alatorre,Raquel Sánchez-Andrés,Santos Cirujano,Santiago Beguería,Salvador Sánchez-Carrillo
Remote Sensing , 2011, DOI: 10.3390/rs3081568
Abstract: In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non?mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.
Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data  [PDF]
Soe W. Myint,May Yuan,Randall S. Cerveny,Chandra P. Giri
Sensors , 2008, DOI: 10.3390/s8021128
Abstract: Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.
Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data
Soe W. Myint,May Yuan,Randall S. Cerveny,Chandra P. Giri
Sensors , 2008,
Abstract: Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.
Fuzzy segmentation of urban areas in panchromatic remote sensing images
全色遥感图像中城区的快速模糊分割算法

CHEN Yan,WAN Shou-hong,GONG Yu-chang,
陈雁
,万寿红,龚育昌

计算机应用 , 2008,
Abstract: A fast fuzzy algorithm for the segmentation of urban areas especially in panchromatic remote sensing images was proposed. The algorithm was supposed to be an image previous process for the small targets recognition in immense remote sensing images. Based on the fuzzy set theory, a membership function was introduced according to Bayesian criteria. The process of segmentation was achieved gradually by using appropriate features of urban areas. The experiment was implemented on SPOT-5 PAN images and a reliability analysis of the fuzzy membership function was made after the segmentation. Compared with the region growing segmentation method and the algorithm based on multi-scale wavelet geometry information, the experimental results show that this algorithm has good performance with low complexity and high accuracy, and it is an efficient preprocessing technique for some important remote sensing applications.
Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas  [PDF]
Abdelhamid A. Elnaggar,Jay S. Noller
Remote Sensing , 2010, DOI: 10.3390/rs2010151
Abstract: Expert assessments for crop and range productivity of very-large arid and semiarid areas worldwide are ever more in demand and these studies require greater sensitivity in delineating the different grades or levels of soil salinity. In conjunction with field study in arid southeastern Oregon, we assess the merit of adding decision-tree analysis (DTA) to a commonly used remote-sensing method. Randomly sampled surface soil horizons were analyzed for saturation percentage, field capacity, pH and electrical conductivity (EC). IFSAR data were acquired for terrain analysis and surficial geological mapping, followed by derivation of layers for analysis. Significant correlation was found between EC values and surface elevation, bands 1, 2, 3 and 4 of the Landsat TM image, and brightness and wetness indices. Maximum-likelihood supervised classification of the Landsat images yields two salinity classes: non-saline soils (EC < 4 dSm–1), prediction accuracy of 97%, and saline soils (EC < 4 dSm–1), prediction accuracy 60%. Addition of DTA results in successful prediction of five classes of soil salinity and an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data only compared to that predicted by additionally using DTA. DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis.
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