• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.363-370
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• 2003

This paper describes a rectification procedure that relies on a polynomial model derived from the imaging geometry without loss of accuracy. By using polynomial model, one can effectively eliminate the iterative process to find an image pixel corresponding to each output grid point. With the imaging geometry and ephemeris data, a geo-location polynomial can be constructed from grid points that are produced by solving three equations simultaneously. And, in order to correct the local distortions induced by the geometry and terrain height, a distortion model has been incorporated in the procedure, which is a function of incidence angle and height at each pixel position. With this function, it is straightforward to calculate the pixel displacement due to distortions and then pixels are assigned to the output grid by re-sampling the displaced pixels. Most of the necessary information for the construction of polynomial model is available in the leader file and some can be derived from others. For validation, sample images of ERS-l PRI and Radarsat-l SGF have been processed by the proposed method and evaluated against ground truth acquired from 1:25,000 topography maps.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.57-59
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• 2004

This paper reports the observation of the interferometric synthetic aperture radar (InSAR) phase anomaly on a newly reclaimed mudflat, Hwaong, in west coast of Korea, detected by a series of Radarsat-l SAR data obtained mostly during 2003. The observed phase anomaly could be from subsidence of mud land caused by volumetric contraction of mud in dry season. This process must have been initiated from March 2002 when tidal water supply to this region was permanently blocked by the newly constructed embankment. The maximum subsidence rate measured from InSAR signal is about 3 cm per month. The local heterogeneity of the subsidence rate over the reclaimed mudflat may indicate various mud composition, surface-subsurface hydrological processes, or subsurface information of the mud and basement rock structure. In-situ measurement must follow to support this observation from space.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.211-219
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• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.460-464
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• 2002

Multitemporal interferometric SAR has became an useful geodetic tool for monitoring Earth's surface deformation, generation of precise DEM, and land cover classification even though there still exist certain constraints such as temporal and spatial decorrelation effects, atmospheric artifacts and inaccurate orbit information. The Korea where nearly all areas are heavily vegetated, JERS-1 SAR has advantages in monitoring surface deformations and environmental changes in that it uses 4-times longer wavelength than ERS-l/2 or RADARSAT SAR system. For generating differential SAR interferogram and differential coherence image fer deformation mapping and temporal change detection, respectively, topographic phase removal process is required utilizing a reference inteferogram or external DEM simulation. Because the SAR antenna baseline parameter for JERS-1 is less accurate than those of ERS-l/2, one can not estimate topographic phases from an external DEM and the residual phase appears in differential interferogram. In this paper, we examined topographic phase retrieval method utilizing an external DEM. The baseline refinement is carried out by minimizing the differences between the measured unwrapped phase and the reference points of the DEM.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.257-272
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• 2007

This paper analyzed relationships between various features from SAR data with multiple acquisition dates and mode (frequency, polarization and incidence angles), and land-cover classes. Two typical types of features were extracted by considering acquisition conditions of currently available SAR data. First, coherence, temporal variability and principal component transform-based features were extracted from multi-temporal and single mode SAR data. C-band ERS-1/2, ENVISAT ASAR and Radarsat-1, and L-band JERS-1 SAR data were used for those features and different characteristics of different SAR sensor data were discussed in terms of land-cover discrimination capability. Overall, tandem coherence showed the best discrimination capability among various features. Long-term coherence from C-band SAR data provided a useful information on the discrimination of urban areas from other classes. Paddy fields showed the highest temporal variability values in all SAR sensor data. Features from principal component transform contained particular information relevant to specific land-cover class. As features for multiple mode SAR data acquired at similar dates, polarization ratio and multi-channel variability were also considered. VH/VV polarization ratio was a useful feature for the discrimination of forest and dry fields in which the distributions of coherence and temporal variability were significantly overlapped. It would be expected that the case study results could be useful information on improvement of classification accuracy in land-cover classification with SAR data, provided that the main findings of this paper would be confirmed by extensive case studies based on multi-temporal SAR data with various modes and ground-based SAR experiments.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.368-368
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• 2008

Oil spills are a principal factor of the ocean pollution. The complicated problems involved in detecting oil spills are usually due to varying wind and sea surface condition such as ocean wave and current. The Hebei Spirit accident was happened in the west sea ($36^{\circ}$41'04" N, $126^{\circ}$03'12" E) near about 8 km distant from Tae-An, Korea on December 7, 2007. The aim of this work is to improve the detection and classification performance in order to define a more accurate training set and identifying the feature of oil spill region. This paper deals with an optimization technique for the detection and classification scheme using multi-frequency and multi-polarization SAR and optical image data sets of the oil spilled sea. The used image data are the ENVISAT ASAR WS and Radarsat-1 of C-band and ALOS PALSAR of L-band SAR data and KOMPSAT-2 optical images together with meteorological or oceanographic data. Both the theory and the experimental results obtained are discussed.