• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.895-902
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• 2022

Building segmentation using satellite imagery such as EO (Electro-Optical) and SAR (Synthetic-Aperture Radar) images are widely used due to their various uses. EO images have the advantage of having color information, and they are noise-free. In contrast, SAR images can identify the physical characteristics and geometrical information that the EO image cannot capture. This paper proposes a learning framework for efficient building segmentation that consists of a teacher-student-based privileged knowledge distillation and deformable convolution block. The teacher network utilizes EO and SAR images simultaneously to produce richer features and provide them to the student network, while the student network only uses EO images. To do this, we present objective functions that consist of Kullback-Leibler divergence loss and knowledge distillation loss. Furthermore, we introduce deformable convolution to avoid pixel-level noise and efficiently capture hard samples such as small and thin buildings at the global level. Experimental result shows that our method outperforms other methods and efficiently captures complex samples such as a small or narrow building. Moreover, Since our method can be applied to various methods.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.1-10
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• 2023

Open surface water body extraction is gaining popularity in recent years due to its versatile applications. Multiple techniques are used for water detection based on applications. Different applications of Radar as LADAR, Ground-penetrating, synthetic aperture, and sounding radars are used to detect water. Shortwave infrared, thermal, optical, and multi-spectral sensors are widely used to detect water bodies. A stereo camera is another way to detect water and different methods are applied to the images of stereo cameras such as deep learning, machine learning, polarization, color variations, and descriptors are used to segment water and no water areas. The Satellite is also used at a high level to get water imagery and the captured imagery is processed using various methods such as features extraction, thresholding, entropy-based, and machine learning to find water on the surface. In this paper, we have summarized all the available methods to detect water areas. The main focus of this survey is on water detection especially in small patches or in small areas. The second aim of this survey is to detect water hazards for unmanned vehicles and off-sure navigation.

• Proceedings of KOSOMES biannual meeting
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• 2004.11a
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• pp.43-47
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• 2004

Two different sensors (here, KOMPSAT and RADARSAT) are considered for ship detection, and are used to delineate the detection performance for their data The experiments are set for coastal regions of Mokpo Port and Ulsan Port and field experiments on board pilot boat are conducted to collect in situ ship validation information such as ship type and length This paper introduce mainly the experiment result of ship detection by both RADARSAT SAR imagery and land-based RADAR data, operated by the local Authority of South Korean, so called vessel traffic system (VTS) radar. Fine imagery of Ulsan Port was acquired on June 19, 2004 and in-situ data such as wind speed and direction, taking pictures of ships and natural features were obtained aboard a pilot ship. North winds, with a maximum speed of 3.1 m/s were recorded Ship's position, size and shape and natural features of breakwaters, oil pipeline and alongside ship were compared using SAR and VTS. It is shown that KOMPSAT/EOC has a good performance in the detection of a moving ship at a speed of kts or more an hour that ship and its wake can be imaged. The detection capability of RADARSAT doesn't matter how fast ship is running and depends on a ship itself, e.g. its material, length and type. Our results indicate that SAR can be applicable to automated ship detection for a VTS and SAR combination service.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1371-1388
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• 2023

Floods are becoming more severe and frequent due to global warming-induced climate change. Water disasters are rising in Korea due to severe rainfall and wet seasons. This makes preventive climate change measures and efficient water catastrophe responses crucial, and synthetic aperture radar satellite imagery can help. This research created 1,423 water body learning datasets for individual water body regions along the Han and Nakdong waterways to reflect domestic water body properties discovered by Sentinel-1 satellite radar imagery. We created a document with exact data annotation criteria for many situations. After the dataset was processed, U-Net, a deep learning model, analyzed water body detection results. The results from applying the learned model to water body locations not involved in the learning process were studied to validate soil water body monitoring on a national scale. The analysis showed that the created water body area detected water bodies accurately (F1-Score: 0.987, Intersection over Union [IoU]: 0.955). Other domestic water body regions not used for training and evaluation showed similar accuracy (F1-Score: 0.941, IoU: 0.89). Both outcomes showed that the computer accurately spotted water bodies in most areas, however tiny streams and gloomy areas had problems. This work should improve water resource change and disaster damage surveillance. Future studies will likely include more water body attribute datasets. Such databases could help manage and monitor water bodies nationwide and shed light on misclassified regions.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1124-1126
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• 2003

The processes of regional scales in the East Korean coastal waters were investigated by analysis of the Synthetic Aperture Radar (SAR) images taken by the European Research Satellites ERS-1, ERS-2 and Envisat. More than 500 quick look frames taken in 1991-2003 were examined to detect the frames with clearly surface expressions of oceanic phenomena. 26 ERS-1/2 SAR and 11 Envisat wide swath Advanced SAR (ASAR) frames were selected and obtained from the European Space Agency in a form of the precision high-resolution images. The following oceanic phenomena and processes were evident in the radar imagery through the Korean costal waters: fronts, currents, eddies, internal waves, island and ship wakes, oil pollution, etc. They manifested themselves in the field of sea surface roughness, their scale ranged from several tens meters to about 100 km. The most common morphology of these phenomena was a series of contrast dark or light curvilinear lines and bands. The joint analysis of the discussed SAR images with other satellite and in situ data supported and enhanced our interpretation of SAR signatures.

• Korean Journal of Remote Sensing
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• v.23 no.1
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• pp.65-69
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• 2007

The processes of regional scales in the East Korean coastal waters were investigated by analysis of the Synthetic Aperture Radar (SAR) images taken by the European Research Satellites ERS-1, ERS-2 and Envisat. More than 500 quick look frames taken in 1991-2003 were examined to detect the frames with clearly surface expressions of oceanic phenomena. 26 ERS-1/2 SAR and 11 Envisat wide swath Advanced SAR (ASAR) frames were selected and obtained from the European Space Agency in a form of the precision high-resolution images. The following oceanic phenomena and processes were evident in the radar imagery through the Korean costal waters: fronts, currents, eddies, internal waves, island and ship wakes, oil pollution, etc. They manifested themselves in the field of sea surface roughness, their scale ranged from several tens meters to about 100 km. The most common morphology of these phenomena was a series of contrast dark or light curvilinear lines and bands. The joint analysis of the discussed SAR images with other satellite and in situ data supported and enhanced our interpretation of SAR signatures.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.12-21
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• 2021

For successful automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, SAR target images of the database should have the identical or highly similar resolution with those collected from SAR sensors. However, it is time-consuming or infeasible to construct the multiple databases with different resolutions depending on the operating SAR system. In this paper, an approach for resolution conversion of SAR target images is proposed based on conditional generative adversarial network(cGAN). First, a number of pairs consisting of SAR target images with two different resolutions are obtained via SAR simulation and then used to train the cGAN model. Finally, the model generates the SAR target image whose resolution is converted from the original one. The similarity analysis is performed to validate reliability of the generated images. The cGAN model is further applied to measured MSTAR SAR target images in order to estimate its potential for real application.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.3
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• Ocean and Polar Research
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• v.31 no.1
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• pp.111-119
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• 2009

Biogenic surface films, which are often present in coastal areas, may enhance the signatures of hydrodynamic processes in microwave, optical, and infrared imagery. We analyzed ERS-1/2 Synthetic Aperture Radar (SAR) and Envisat Advanced Synthetic Aperture Radar (ASAR) images taken over the Japan/East Sea (JES). We focused on the appearance of the contrast SAR signatures, particularly the dark features of different scales caused by various oceanic and atmospheric phenomena. Spiral eddies of different scales were detected through surface film patterns both near the coast and in the open regions of the JES in warm and cold seasons. During field experiments carried out at the Pacific Oceanological Institute (POI) Marine Station 'Cape Shults' in Peter the Great Bay, the sea surface roughness characteristics were measured during the day and night using a developed polarization spectrophotometer and various digital cameras and systems of floats. The velocity of natural and artificial slicks was estimated using video and ADCP time series of tracers deployed on the sea surface. The slopes of gravity-capillary wave power spectra varied between .4 and .5. Surface currents in the natural and artificial slicks increased with the distance from the coast, varying between 4 and 40 cm/s. The contrast of biogenic and anthropogenic slicks detected on vertical and horizontal polarization images against the background varied over a wide range. SAR images and ancillary satellite and field data were processed and analyzed using specialized GIS for marine coastal areas.