Oil spill detection from SAR image using SVM based classification

In this paper, the potential of fully polarimetric L-band SAR data for detecting sea oil spills is investigated using polarimetric decompositions and texture analysis based on SVM classifier. First, power and magnitude measurements of HH and VV polarization modes and, Pauli, Freeman and Krogager decompositions are computed and applied in SVM classifier. Texture analysis is used for identification using SVM method. The texture features i.e. Mean, Variance, Contrast and Dissimilarity from them are then extracted. Experiments are conducted on full polarimetric SAR data acquired from PALSAR sensor of ALOS satellite on August 25, 2006. An accuracy assessment indicated overall accuracy of 78.92% and 96.46% for the power measurement of the VV polarization and the Krogager decomposition respectively in first step. But by use of texture analysis the results are improved to 96.44% and 96.65% quality for mean of power and magnitude measurements of HH and VV polarizations and the Krogager decomposition. Results show that the Krogager polarimetric decomposition method has the satisfying result for detection of sea oil spill on the sea surface and the texture analysis presents the good results

SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA

Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds in a hierarchical scheme that is uses a cross-validation technique. The algorithm is tested on a dataset including many single and cluster outliers. Our algorithm can deal with both the irregular LiDAR point clouds and the regular grid data. Experimental results show that the presented algorithm almost completely detects both the single and cluster outliers, but some inlier points are wrongly removed as outlier. An accuracy assessment indicated 0.018% Error α and, 0.352% Error β that are very satisfactory

Estimation of vegetation fraction in arid areas using ALOS imagery

Fraction of vegetation (Fv) plays an important role in ecosystems. Estimation of Fv is essential for drought monitoring, natural resources studies, estimation of soil erosion volume etc. The aim of this study is to estimate Fv in an arid area in Iran using ALOS Imagery (June 2008). In order to find the best index for estimation of Fv, Seventeen vegetation indices (ARVI, DVI, EVI, GEMI, IPVI, MSAVI1, MSAVI2, NDVI, PVI, SAVI, SARVI, SARVI2, SR, TSAVI, WDVI) were used. The canopy cover percentage of 52 sample plots (50m by 50m) was measured in the field in June 2009. Regression models were used to assess the relationships between the field data and the calculated Fv. The 52 sample plots were randomly divided two times to 30 calibrations and 22 validations, and to 35 and 17 samples. Results revealed that selecting the calibration and validation data randomly leads to different results. Therefore, cross-validation method was used to reduce random division effect. Results indicated that, among all indices, vegetation indices such as MSAVI1, PVI, WDVI and TSAVI which are based on soil line have higher R2 and lower RMSE (R2 > 0.63, RMSE ˜ 3%). The results confirm the dominant effect of soil reflectance in arid areas

Vertical accuracy assessment of LiDAR ground points using minimum distance approach

Light Detection And Ranging is now being widely used to provide accurate digital elevation model (DEM). One common method used to determine the accuracy of LiDAR - Light Detection And Ranging - vertical accuracy is compared LiDAR-derived DEM elevation with survey-derived ground control points (GCPs). However, because the DEM elevations are generalised to the areas covered by one cell, inherent errors when compared to the elevation of a GCP are evident. This paper presents a method based on a minimum distance approach using the so called first law of geography to assess the accuracy of LiDAR ground point dataset. The result has shown that the tested LiDAR ground point dataset is suitable for applications that do not need a vertical accuracy better than 0.5m

GAP FILLING IN ROAD EXTRACTION USING RADON TRANSFORMATION

Road information has a key role in many applications such as transportation, automatic navigation, traffic management, crisis management, and also to facilitate and accelerate updating databases in a GIS. Therefore in the past two decades, automatic road extraction has become an important issue in remote sensing, photogrammetry and computer vision. An essential challenge in road extraction process is filling the gaps which have appeared due to getting placed under trees, tunnels or any other reason. Connection of roads is a momentous topological property that is necessity to perform most of the spatial analyses. Hence, Gap filling is an important post-process. The main aim of this paper is to provide a method which is applicable in road extraction algorithms to automatic fill the gaps. The proposed algorithm is based on Radon transformation and has four stags. In the first stage, detected road are thinned insofar as one pixel width is achieved. Then endpoints are detected. In the second stage, regarding to some constraints those endpoints which do not belong to any gaps are identified and deleted from endpoints list. In the third stage, the real gaps are found using the road direction computed by used of Radon technique. In fourth stage, the selected endpoints are connected together using Spline interpolation. This algorithm is applied on several datasets and also on a real detected road. The experimental results show that the proposed algorithm has good performance on straight roads but it does not work well in intersections, due to being direction-oriented