• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.361-369
• /
• 1998

Recently, the improvement of on-board satellite sensors covering hyperspectral image sensors, high spatial resolution sensors provide data on earth in diverse aspect. The application field relating remotely sensed data also varies depending on what type of job one wants. The various resolution of sensors from low to extremely high is also available on the market with a user defined specific location. The expense to purchase remote sensed data is going down compare to the cost it need past few years ago in terms of research or private use. Now, the satellite remote sensed data is used on the field of forecasting, forestry, agriculture, urban reconstruction, geology, or other research field in order to extract meaningful information by applying special techniques of image processing. There are many image processing packages available worldwide and one common aspect is that they are expensive. There need to be a advanced satellite data processing package for people who can not afford commercial packages to apply special remote sensing techniques on their data and produce valued-added product. The study was carried out with the purpose of developing a special satellite data processing package which covers almost every satellite produced data with normal image processing functions and also special functions needed on specific research field with friendly graphical user interface (GUI). And for the people with any background of remote sensing with windows platform.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.514-519
• /
• 1992

To make an accurate retrieval of the proportion of each category among mixed pixels (Mixel's) of a remotely sensed imagery, a maximum likelihood estimation method of category proportion is proposed. In this method, the observed multispectral vector is considered as probability variables along with the approximation that the supervised data of each category can be characterized by normal distribution. The results show that this method can retrieve accurate proportion of each category among Mixel's. And a index that can estimate the degree of error in each category is proposed. AS one of the application of the proportion estimation, a method for image classification based on category proportion estimation is proposed. In this method all pixel in a remotely sensed imagery are assumed to be Mixel's, and are classified to most dominant category. Among the Mixel's, there exists unconfidential pixels which should be categorized as unclassified pixels. In order to discriminate them, two types of criteria, Chi square and AIC, are proposed for fitness test on pure pixel hypothesis. Experimental result with a simulated dataset show an usefulness of proposed classification criterion compared to the conventional maximum likelihood criterion and applicability of the fitness tests based on Chi square and AIC,

• The Journal of Engineering Geology
• /
• v.2 no.2
• /
• pp.141-146
• /
• 1992

Synthetic stereo-pair of Landsat TM(Thematic Mapper) and shaded relief image from DEM(Digital Elevation Model) were digitally produced. They are very useful in optimizing the geological interpretability of the remotely sensed data.

• Spatial Information Research
• /
• v.17 no.4
• /
• pp.455-462
• /
• 2009

A method to find the nearly optimal PSNR values for compression was tried to remotely sensed images. There is no rule to find the best wavelet pairs for image processing. The expected wavelet pairs following the suggested algorithm showed the optimal result for various kinds of images. Firstly, the PSNR variations with three wavelet families were analyzed. In many cases the longer wavelet filter shows the higher PSNR value, but the rate is getting less in orthogonal wavelet families. Wavelets with moderate filter length are suggested at the point of computational cost. For biorthogonal families it was hard to predict from the length of filters. Multiresolution wavelet analysis was used up to level 3 with three kinds of wavelet families. Biorthogonal wavelet family showed irregular pattern to get the maximum PSNR values, while orthogonal wavelet families showed regular pattern. In orthogonal wavelet families the nearly optimal wavelet pair can be predicted from the level 1.

• Korean Journal of Remote Sensing
• /
• v.17 no.2
• /
• pp.183-188
• /
• 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.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2019.05a
• /
• pp.152-152
• /
• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• Korean Journal of Remote Sensing
• /
• v.7 no.1
• /
• pp.69-86
• /
• 1991

An unsupervised region based image segmentation algorithm implemented with a pyramid structure has been developed. Rather than depending on thraditional local splitting and merging of regions with a similarity test of region statistics, the algorithm identifies the homogenous and boundary regions at each level of pyramid, then the global parameters of esch class are estimated and updated with values of the homogenous regions represented at the level of the pyramid using the mixture distribution estimation. The image is then classified through the pyramid structure. Classification results obtained for both simulated and SPOT imagery are presented.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.20 no.1
• /
• pp.21-31
• /
• 2002

This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.

• Journal of Korean Society for Geospatial Information Science
• /
• v.11 no.1 s.24
• /
• pp.13-19
• /
• 2003

In this paper we discussed the processing procedures of histogram equalization(HE) method and brightness preserving bi-histogram equalization(BBHE) method in the contrast enhancement methods for the performance comparison. With remotely sensed image data of Landsat TM we compared the performances of three methods of Min-Max method, HE method, BBHE method. The experimental results demonstrate that the HE method and BBHE method are more effective in the contrast enhancement performances than the Min-Max method. In the HE method the mean brightness of the resultant output images approached to the middle gray level with regardless of input image mean. In the BBHE method, it is capable of preserving the mean brightness of a original image compared to the HE method while enhancing the contrast of original image effectively. Thus BBHE method is provided more natural enhancement effect than the HE method.

• Korean Journal of Remote Sensing
• /
• v.18 no.2
• /
• pp.117-125
• /
• 2002

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. Using an adaptive reconstruction system, dynamic compositing approach was developed to recover missing/bad observations. The reconstruction method incorporates temporal variation in physical properties of targets and anisotropic spatial optical properties into image processing. The adaptive system performs the dynamic compositing by obtaining a composite image as a weighted sum of the observed value and the value predicted according to local temporal trend. The proposed system was applied to the sequence of NDVI images of AVHRR observed on the Korean Peninsula from 1999 year to 2000 year. The experiment shows that the reconstructed series can be used as an estimated series with complete data for the observations including bad/missing values. Additionally, the gradient image, which represents the amount of temporal change at the corresponding time, was generated by the proposed system. It shows more clearly temporal variation than the data image series.