• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.422-425
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• 2007

Traditionally, surface energy fluxes are obtained by model simulations or empirical equations with auxiliary meteorological data. These methods may not effectively represent the surface heat fluxes in a regional scale due to scene variability. On the other hand, remote sensing has the advantage to acquire data of a large area in an instantaneous view. The remotely sensed data can be further used to retrieve surface radiation and heat fluxes over a large area. In this study, the airborne and satellite images in conjunction with meteorological data and ground observations were used to estimate the surface heat fluxes over Taiwan's Chaiyi Plain. The results indicate that surface heat fluxes can be properly determined from both airborne and satellite images. The correlation coefficient of surface heat fluxes with in situ corresponding observations is over 0.60. We also observe that the remotely sensed data can efficiently provide a long term monitoring of surface heat fluxes over Taiwan's Chaiyi Plain.

• Korean Journal of Remote Sensing
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• 제19권6호
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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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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.530-535
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• 2002

An approach for quickly updating GIS building data using high resolution remotely sensed data is proposed in this paper. High resolution remotely sensed data could be aerial photographs, satellite images and airborne laser scanning data. Data from different types of sensors are integrated in building extraction. Based on the extracted buildings and the outdated GIS database, the change-detection-template can be automatically created. Then, GIS building data can be fast updated by semiautomatically processing the change-detection-temp late. It is demonstrated that this approach is quick, effective and applicable.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.47-49
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• 2003

Automatic land use and land cover change (LUCC) detection via remotely sensed imagery has a wide application in the area of LUCC research, nature resource and environment monitoring and protection. Under the condition that one time (T1) data is existed land use and land cover maps, and another time (T2) data is remotely sensed imagery, how to detect change automatically is still an unresolved issue. This paper developed a land use and land cover class knowledge guided method for automatic change detection under this situation. Firstly, the land use and land cover map in T1 and remote sensing images in T2 were registered and superimposed precisely. Secondly, the remotely sensed knowledge database of all land use and land cover classes was constructed based on the unchanged parcels in T1 map. Thirdly, guided by T1 land use and land cover map, feature statistics for each parcel or pixel in RS images were extracted. Finally, land use and land cover changes were found and the change class was recognized through the automatic matching between the knowledge database of remote sensing information of land use & land cover classes and the extracted statistics in that parcel or pixel. Experimental results and some actual applications show the efficiency of this method.

• Korean Journal of Remote Sensing
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• 제23권5호
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• pp.355-363
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• 2007

The 3D DWT(The Three Dimensional Discrete Wavelet Transform) scheme is potentially regarded as useful one on analyzing both spatial and spectral information. Nevertheless, few researchers have attempted to process or classified remotely sensed images using the 3D DWT. This study aims to apply the 3D DWT to the land cover classification of optical and SAR(Synthetic Aperture Radar) images. Then, their results are evaluated quantitatively and compared with the results of traditional classification technique. As the experimental results, the 3D DWT shows superior classification results to conventional techniques, especially dealing with the high-resolution imagery and SAR imagery. It is thought that the 3D DWT scheme can be extended to multi-temporal or multi-sensor image classification.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1284-1286
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• 2003

There is a growing demand on remotely sensed and GIS data services in modern society. However, conventional WEB applications based on client/server pattern can not meet the criteria in the future . Grid computing provides a promising resolution for establishing spatial information system toward future applications. Here, a new architecture of the distributed environment for remotely sensed data processing based on the middleware technology was proposed. In addition, in order to utilize the new environment, a problem had to be algorithmically expressed as comprising a set of concurrently executing sub-problems or tasks. Experiment of the algorithm was implemented, and the results show that the new environmental can achieve high speedups for applications compared with conventional implementation.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.319-321
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• 2003

It is our goal to obtain a better scientific understanding of how to define the nature and role of remotely sensed land surface parameters and energy fluxes in the heat island phenomena, and local and regional weather and climate. By using the TRMM (Tropical Rainfall Measuring Mission) visible and thermal imagery data and analyzing the surface energy flux images associated with the change of the landcover and land use in the study area, we present how significant is the magnitude of the heat island heat effect and its relation with the surface parameters and the energy fluxes in the Taichung area of Taiwan. We used the energy budget components such as net radiation, soil heat flux, sensible heat flux, and latent heat flux in the study area of interest derived form remotely sensed data to understand the island heat effect in Taichung. The results show that water is the most important component to decrease the temperature, and the more the consumed net radiation to latent heat, the lower the urban surface temperature.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• Korean Journal of Remote Sensing
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• 제20권3호
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• pp.215-225
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• 2004

A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure of CN-chain with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using the conventional agglomerative approach. Using simulation data, the proposed method was compared with another hierarchical clustering technique based on 'mutual closest neighbor.' The experimental results show that the new approach proposed in this study considerably increases in computational efficiency for larger images with a low number of bands. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.

• Korean Journal of Remote Sensing
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• 제17권2호
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.