• Korean Journal of Remote Sensing
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• v.20 no.4
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• pp.275-288
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• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.885-887
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• 2003

The purpose of this investigation is to generate true orthophotos from high resolution satellite images. The major works of this research include 4 parts: (1) determination of orientation parameters, (2) generating traditional orthophotos using terrain model, (3) relief correction for buildings, and (4) process for hidden areas. To determine the position of satellites, we correct the onboard orientation parameters to fine tune the orbit. In the generation of traditional orthophotos, we employ orientation parameters and digital terrain model(DTM) to rectify tilt displacements and relief displacements for terrain. We, then, compute relief displacements for buildings with digital building model (DBM). To avoid double mapping, we detect hidden areas. Due to the satellite’s small field of view, an efficient method for the detection of hidden areas and building rectification will be proposed in this paper. Test areas cover the city of Kaohsiung in southern Taiwan. Test images are from the QuickBird satellite.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.561-570
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• 2010

The purpose of this study is to develop an Android smartphone app providing analysis capabilities of remote sensing images, by using mobile browsing open sources of gvSIG, open source remote sensing software of OTB and open source DBMS of PostgreSQL. In this app, five kinds of remote sensing algorithms for filtering, segmentation, or classification are implemented, and the processed results are also stored and managed in image database to retrieve. Smartphone users can easily use their functions through graphical user interfaces of app which are internally linked to application server for image analysis processing and external DBMS. As well, a practical tiling method for smartphone environments is implemented to reduce delay time between user's requests and its processing server responses. Till now, most apps for remotely sensed image data sets are mainly concerned to image visualization, distinguished from this approach providing analysis capabilities. As the smartphone apps with remote sensing analysis functions for general users and experts are widely utilizing, remote sensing images are regarded as information resources being capable of producing actual mobile contents, not potential resources. It is expected that this study could trigger off the technological progresses and other unique attempts to develop the variety of smartphone apps for remote sensing images.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.476-483
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• 2002

In the past 20 years or so, numerous methods to reduce speckle in SAR images have been proposed. The primary goal of these methods is to reduce speckle without destroying resolution and smearing edge information. But the experiments indicate that there is always a kind of tradeoff between smoothing out speckle and preserving edge information. In this paper, an optimal SAR speckle filter is developed. It can effectively smooth out speckle while preserve edge information.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.310-313
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• 2004

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• Proceedings of the KSRS Conference
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• v.1
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• pp.239-242
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• 2006

Impervious surface is an important index for the estimation of urbanization and environmental change. In addition, impervious surface has an influence on the parameters of rainfall-runoff model during rainy season. The increase of impervious surface causes peak discharge increasing and fast concentration time in urban area. Accordingly, impervious surface estimation is an important factor of urban rainfall-runoff model development and calibration. In this study, impervious surface estimation is performed by using remote sensing images such as landsat-7 ETM+ and high resolution satellite image and regression tree algorithm based on case study area ? Jungnang-cheon basin in Korea.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.888-890
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• 2003

The objective of this investigation is to establish a simple yet effective block adjustment procedure for the orthorectification of multi-orbit satellite images. The major works of the proposed scheme are: (1) adjustment of satellite‘s orbit accurately, (2) calculation of the error vectors for each tie point using digital terrain model and ray tracing technique, (3) refining the orbit using the Least Squares Filtering technique and (4) generation of the orthophotos. In the process of least squares filtering, we use the residual vectors on ground control points and tie points to collocate the orbit. In orthorectification, we use the indirect method to generate the orthoimage. Test areas cover northern Taiwan. Test images are from SPOT 5 satellite. Experimental results indicate that proposed method improves the relative accuracy significantly.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.408-410
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• 2003

In this paper, an improved gradient direction assisted linking algorithm is proposed. This algorithm begins with initial seeds satisfying some local criteria. Then it will search along the direction provided by the initial point. A window will be generated in the gradient direction of the current point. Instead of the conventional method which only considers the value of the local salient structure, an improved mathematical model is proposed to describe the desired linear features. This model not only considers the value of the salient structure but also the direction of it. Furthermore, the linking problem under this model can be efficiently solved by dynamic programming method. This algorithm is tested for linear features detection in IKONOS images. The result demonstrates this algorithm is quite promising.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.135-139
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• 1999

Wind velocity is one of the primary variables for describing atmospheric state from GMS-5. And its accurate depiction is essential for operational weather forecasting and for initialization of NWP(Numerical Weather Prediction) models. The aim of this research is to incorporate imagery from other available spectral channels and examine the error characteristics of winds derived from these images. Multi spectral imagery from GMS-5 was used for this purpose and applied to Korean region with together BoM(Bureau of Meteorology). The derivation of wind velocity estimates from low and high resolution visible, split window infrared, and water vapor images, resulted in improvements in the amount and quality of wind data available for forecasting.