• 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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• 2005.10a
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• pp.47-51
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• 2005

Using satellite images, an optimal solution to water motion has been presented in this study. Since temperature patterns are suitable tracers in water motion, Sea Surface Temperature (SST) images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Two daily SST images with 24 hours time interval are used as input data. Computation of templates correspondence between pairs of images is crucial within motion algorithms using non-rigid body objects. Image matching methods have been applied to estimate water body motion within the two SST images. The least squares matching technique, as a flexible technique for most data matching problems, offers an optimal spatial solution for the motion estimation. The algorithm allows for simultaneous local radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between the two image templates is modeled both geometrically and radiometrically. Geometric component of the model includes six geometric transformation parameters and radiometric component of the model includes two radiometric transformation parameters. Using the algorithm, the parameters are automatically corrected, optimized and assessed iteratively by the least squares algorithm. The method used in this study, has presented more efficient and robust solution compared to the traditional motion estimation schemes.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.73-79
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• 2016

Remote sensing and measurement are of paramount importance of providing information on the state of water quality in water bodies. The formation and growth of cyanobacteria is of serious concern to in land aquatic life forms and human life. The main cause of water quality deterioration stems from anthropogenic induced eutrophication. The goal of this research to quantify and determine the spatial distribution of cyanobacteria concentration in the water using remote sensing technique. The standard approach to measure water quality based on the direct measurement of the fluorescence of the chlorophyll a in the living algal cells and the same approach used to detect the phycobilin pigments found in blue-green algae (a.k.a. cyanobacteria), phycocyanin and phycoerythrin. This paper propose the emerging sensor design to measure the water quality based on the optical analysis by fluorescence of the phycocyanin pigment. In this research, we developed an method to sense and quantify to derive phycocyanin intensity index for estimating cyanobacteria concentrations. The development of the index was based on the reflectance difference between visible light band 620nm and 665nm. As a result of research this paper presents, an optical biological sensor design information to measure the Phycocyanin parameters in water content.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.168-171
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• 2003

We introduced a small-sized automatic model helicopter for using in the remote sensing. The feature of our helicopter system is that the helicopter system is realized using commercialized radio-controlled model helicopter, whose payload is 1.5 kg. Therefore, our system has may benefits when apply our system to practical tasks. The compactness and light-weight of our system is realized by the introduction of our original technique to suppress the vibration noise using a slimy material as well as the introduction of latest sensors and semiconductor products. Submerging the acceleration sensor into a slimy material, moise ratio is highly improved. In this paper, we show an experimental results for the effect of our orignal technique to remove the vibration noise of helicopter. The result of hovering flying test shows the effectiveness of our system.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.329-336
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• 2012

This paper give further assessment on the original DoP-CPD classification scheme. This paper provides some additional comparative study on the DoP-CPD with H/A/alpha classifier in terms of multi look effects and classification performances. The statistics and multi looking effects of the DoP and CPD were analyzed with measured polarimetric SAR data. DoP-CPD is less sensitive to the number of averaging pixels than the entropy-alpha technique. A DoP-CPD diagram with appropriate boundaries between six different classes was then developed based on the data analysis. A polarimetric SAR image DoP-CPD classification technique is verified with C-band polarimetric RADARSAT-2 images.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.17-25
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• 1996

This paper presents a GCP marking technique using scanned maps. Twelve maps with a scale of 1:250, 000 were scanned and stored as raster images. The distortion factors of scanned maps were modelled by polynomials. The coefficients of the polynomials were determined by a least squre fit. This technique does not require a bulky and expensive digitizing table so that it is suitable for a low-cost pre-processing system. The GCP marking using this technique showed a sufficient accuracy for KITSAT1, 2 narrow camera images.

• KCID journal
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• v.2 no.2
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• pp.46-53
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• 1995

• KCID journal
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• v.3 no.1
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• pp.64-74
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• 1996