• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.223-232
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• 2023

A widely used method for monitoring land cover using high-resolution satellite images is to classify the images based on the colors of the objects of interest. In urban areas, not only major objects such as buildings and roads but also vegetation such as trees frequently appear in high-resolution satellite images. However, the colors of vegetation objects often resemble those of other objects such as buildings, roads, and shadows, making it difficult to accurately classify objects based solely on color information. In this study, we propose a method that can accurately classify not only objects with various colors such as buildings but also vegetation objects. The proposed method uses the normalized difference vegetation index (NDVI) image, which is useful for detecting vegetation objects, along with the RGB image and classifies objects into subclasses. The subclass classification results are fused, and the final classification result is generated by combining them with the image segmentation results. In experiments using Compact Advanced Satellite 500-1 imagery, the proposed method, which applies the NDVI and subclass classification together, showed an overall accuracy of 87.42%, while the overall accuracy of the subchannel classification technique without using the NDVI and the subclass classification technique alone were 73.18% and 81.79%, respectively.

• The Korean Journal of Nuclear Medicine
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• v.34 no.1
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• pp.82-93
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• 2000

Purpose: We characterized the signals obtained from the components of a small gamma camera using Nal(Tl)-position sensitive photomultiplier tube (PSPMT) and optimized the parameters employed in the modules of the system. Materials and Methods: The small gamma camera system consists of a Nal(Tl) crystal ($60{\times}60{\times}6mm^3$) coupled with a Hamamatsu R3941 PSPMT, a resister chain circuit, preamplifiers, nuclear instrument modules (NIMs), an analog to digital converter and a personal computer for control and display. The PSPMT was read out using a resistive charge division circuit which multiplexes the 34 cross wire anode channels into 4 signals (X+, X-, Y+, Y -). Those signals were individually amplified by four preamplifiers and then, shaped and amplified by amplifiers. The signals were discriminated and digitized via triggering signal and used to localize the position of an event by applying the Anger logic. The gamma camera control and image display was performed by a program implemented using a graphic software. Results: The characteristics of signal and the parameters employed in each module of the system were presented. The intrinsic sensitivity of the system was approximately $8{\times}10^3$ counts/sec/${\mu}Ci$. The intrinsic energy resolution of the system was 18% FWHM at 140 keV. The spatial resolution obtained using a line-slit mask and $^{99m}Tc$ point source were, respectively, 2.2 and 2.3 mm FWHM in X and Y directions. Breast phantom containing $2{\sim}7mm$ diameter spheres was successfully imaged with a parallel hole collimator. The image displayed accurate size and activity distribution over the imaging field of view Conclusion: We proposed a simple method for development of a small gamma camera and presented the characteristics of the signals from the system and the optimized parameters used in the modules of the small gamma camera.