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This study investigates the effects of glacial lakes on the flow velocities of Duiya Glacier, southern Tibetan Plateau, by employing satellite Synthetic Aperture Radar (SAR) Pixel-Offset-Tracking Small-Baseline-Subset (PO-SBAS) technology. Duiya Glacier, which terminates in a lake, exhibits approximately 10 m/yr higher horizontal speed and a higher melting rate with a vertical Non-Surface-Parallel-Flow (nSPF) velocity of – 7 m/yr than those of its land-terminating counterpart – West Duosangpu Glacier. Notably, Duiya Glacier experiences significant seasonal velocity fluctuations with accelerated flow between May and October. By integrating glacier geometry, changes in glacier boundaries and the extent of proglacial lakes, the distribution of supraglacial lakes, and climatic variables, we reveal that proglacial and supraglacial lakes play a crucial role in increasing the flow velocities of Duiya Glacier. Duiya Glacier flows faster because of increased subglacial water pressure resulting from water influx from these lakes. This phenomenon becomes conspicuously evident during May – October, when increased meltwater due to increased temperatures and precipitation further elevates the subglacial water pressure. Our method highlights the potential for understanding the impact of glacial lakes on glacier movements at a large scale, leveraging the capabilities of satellite SAR PO-SBAS technology for continuous, wide-scale, high temporal resolution, and 3D velocity monitoring.

Based on remote sensing data obtained by Sentinel-1A and Sentinel-1B satellites in 2016–2022 and the results of analysis of hydrocarbons (aliphatic—AHCs, and polycyclic aromatic—PAHs) in bottom sediments collected in 2019–2022, the nature of oil slicks in various areas of the Barents Sea has been established. It has been shown that the distribution of oil slicks in coastal areas is greatly influenced by anthropogenic hydrocarbon inflows (mainly from shipping), which is confirmed by elevated AHC concentrations in coastal sediments (up to 73 μg/g) and in the composition of Corg (up to 3.6%). In the central and northern regions of the sea (in particular, at coordinates 75.2°–75.3° N and 31.5°–31.8° E, station 7105), the grouping of oil slicks is due to natural oil and gas manifestations. This is confirmed by the anomalous concentration of PAHs in the lower horizons of the sediment column, as well as their composition (dominance of 2-methylnaphthalene, a marker of their oil genesis). At the same time, the proportion of light homologues in the composition of alkanes increased, which may indicate their formation in the sedimentary sequence. [ABSTRACT FROM AUTHOR]

This study presents an efficient super-resolution (SR) method for targets observed by satellite synthetic aperture radar (SAR). First, a small target image is extracted from a large-scale SAR image and undergoes proper preprocessing. The preprocessing step is adaptively designed depending on the types of movements of targets. Next, the principal scattering centers of targets are extracted using the compressive sensing technique. Subsequently, an impulse response function (IRF) of the satellite SAR system (IRF-S) is generated using a SAR image of a corner reflector located at the calibration site. Then, the spatial resolution of the IRF-S is improved by the spectral estimation technique. Finally, according to the SAR signal model, the super-resolved IRF-S is combined with the extracted scattering centers to generate a super-resolved target image. In our experiments, the SR capabilities for various targets were investigated using quantitative and qualitative analysis. Compared with conventional SAR SR methods, the proposed scheme exhibits greater robustness towards improvement of the spatial resolution of the target image when the degrees of SR are high. Additionally, the proposed scheme has faster computation time (CT) than other SR algorithms, irrespective of the degree of SR. The novelties of this study can be summarized as follows: (1) the practical design of an efficient SAR SR scheme that has robustness at a high SR degree; (2) the application of proper preprocessing considering the types of movements of targets (i.e., stationary, moderate motion, and complex motion) in SAR SR processing; (3) the effective evaluation of SAR SR capability using various metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), focus quality parameters, and CT, as well as qualitative analysis. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) obtains two-dimensional images of the Earth’s surface. Spatial resolution is one of the most important factors in evaluating SAR performance. Therefore, high operating frequencies are preferred for obtaining high resolution images. In fact, Ku-band and Ka-band are used in the automobile SAR (Auto-SAR) and the aircraft SAR (Airborne-SAR). However, the operating frequencies of the satellite SAR (Spaceborne-SAR) are limited to the X-band because of the wide Doppler frequency band caused by the satellite’s high velocity. To overcome this frequency limitation in satellite SAR, we propose a low-complexity SAR algorithm that forms images of the ocean using satellite altimeter data with an operating frequency of the Ku-band (13.575 GHz). The algorithm requires the burst mode pulse transmission technique frequently used in SAR altimeters. To validate the proposed algorithm as being suitable for ocean image formation, point target simulation and analysis are performed, and some quantitative results are provided.

Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target's motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors' knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition. [ABSTRACT FROM AUTHOR]

In this study, when developing a phased array feeder for the verification of dual reflector antennas for satellite synthetic aperture radar (SAR) applications, the main component test and performance verification of the feeder were considered. The performance was verified at the level of the component radiator, radiofrequency/control board, and integrated phased array feeder with specifications, including the bandwidth, polarization, directivity, beam width, and side lobe, required for the feeder. Accordingly, it was confirmed that the bandwidth was 19.18 % (H) and 16.07 % (V), directivity was 38.42 dBi (H), 38.32 dBi (V), HPBW Az/El 2.064° (H)/2.187° (H), 2.083° (V)/2.202° (V), and the side lobe was under −12.84 dB (H), −13.05 dB (V). Therefore, we verified that it can be adopted as a phased array feeder for the verification of dual reflector antennas for satellite SAR applications. [ABSTRACT FROM AUTHOR]

Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD dataset using KOMPSAT-5 images. The dataset is currently available through the website, aihub.or.kr.

This study presents an efficient super-resolution (SR) method for targets observed by satellite synthetic aperture radar (SAR). First, a small target image is extracted from a large-scale SAR image and undergoes proper preprocessing. The preprocessing step is adaptively designed depending on the types of movements of targets. Next, the principal scattering centers of targets are extracted using the compressive sensing technique. Subsequently, an impulse response function (IRF) of the satellite SAR system (IRF-S) is generated using a SAR image of a corner reflector located at the calibration site. Then, the spatial resolution of the IRF-S is improved by the spectral estimation technique. Finally, according to the SAR signal model, the super-resolved IRF-S is combined with the extracted scattering centers to generate a super-resolved target image. In our experiments, the SR capabilities for various targets were investigated using quantitative and qualitative analysis. Compared with conventional SAR SR methods, the proposed scheme exhibits greater robustness towards improvement of the spatial resolution of the target image when the degrees of SR are high. Additionally, the proposed scheme has faster computation time (CT) than other SR algorithms, irrespective of the degree of SR. The novelties of this study can be summarized as follows: (1) the practical design of an efficient SAR SR scheme that has robustness at a high SR degree; (2) the application of proper preprocessing considering the types of movements of targets (i.e., stationary, moderate motion, and complex motion) in SAR SR processing; (3) the effective evaluation of SAR SR capability using various metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), focus quality parameters, and CT, as well as qualitative analysis.

In this study, we analyzed the performance of a refocusing algorithm that compensates for the blurring effect of a moving target in a synthetic aperture radar image. First, sub-images containing blurred target signals were extracted from the images obtained from the KOrea Multi-Purpose SATellite-5 (KOMPSAT-5). Subsequently, some refocusing algorithms were applied to the sub-images to compensate for the blurring effect of the moving target, and their performance was analyzed. [ABSTRACT FROM AUTHOR]

Abstract Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data are a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes.

Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target’s motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors’ knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition.

Abstract Coseismic and postseismic crustal deformations caused by earthquake episodes are important in understanding the mechanisms of these episodes as well as the fault rheology near an epicentral area. Specifically, interferometric synthetic aperture radar (InSAR) and synthetic aperture radar (SAR) pixel tracking can depict high‐resolution crustal deformation fields associated with earthquakes and volcanic activities without installing on‐site observation instruments. In this study, we investigate the coseismic and postseismic near‐fault crustal deformations associated with the 2016 Kumamoto earthquake sequence in southwest Japan using ALOS‐2/PALSAR‐2 (Phased Array‐type L‐band SAR‐2) data. Coseismic three‐dimensional (3‐D) displacement fields inferred from PALSAR‐2 pixel tracking data showed 1.6 m of horizontal displacements and 2 m of subsidence at maximum, despite the mainshock focal mechanism that was dominated by strike‐slip components with N‐S extension axes. The locations of large displacement variations along surface ruptures due to the mainshock were almost identical to a region where infrastructure was damaged, thus implying the generation of strong ground seismic waves. By using conventional InSAR stacking, we inferred two independent quasi‐east‐west and quasi‐vertical displacement fields as cumulative postseismic deformations following the mainshock over a period of about 2 years. The near‐fault postseismic deformation represented complicated displacement characteristics that could not be explained by a single physical process. Deformation signals around Aso volcano were interpreted by the effects of postseismic physical processes as well as geological heterogeneous structures.

Coseismic and postseismic crustal deformations caused by earthquake episodes are important in understanding the mechanisms of these episodes as well as the fault rheology near an epicentral area. Specifically, interferometric synthetic aperture radar (InSAR) and synthetic aperture radar (SAR) pixel tracking can depict high‐resolution crustal deformation fields associated with earthquakes and volcanic activities without installing on‐site observation instruments. In this study, we investigate the coseismic and postseismic near‐fault crustal deformations associated with the 2016 Kumamoto earthquake sequence in southwest Japan using ALOS‐2/PALSAR‐2 (Phased Array‐type L‐band SAR‐2) data. Coseismic three‐dimensional (3‐D) displacement fields inferred from PALSAR‐2 pixel tracking data showed 1.6 m of horizontal displacements and 2 m of subsidence at maximum, despite the mainshock focal mechanism that was dominated by strike‐slip components with N‐S extension axes. The locations of large displacement variations along surface ruptures due to the mainshock were almost identical to a region where infrastructure was damaged, thus implying the generation of strong ground seismic waves. By using conventional InSAR stacking, we inferred two independent quasi‐east‐west and quasi‐vertical displacement fields as cumulative postseismic deformations following the mainshock over a period of about 2 years. The near‐fault postseismic deformation represented complicated displacement characteristics that could not be explained by a single physical process. Deformation signals around Aso volcano were interpreted by the effects of postseismic physical processes as well as geological heterogeneous structures. Plain Language Summary: Coseismic and postseismic crustal deformation fields provide information for inferring slip distributions occurring along a fault, and these data are helpful for understanding the geological characteristics around an epicentral area. By using satellite synthetic aperture radar (SAR) images, we can derive crustal deformation maps with high spatial resolutions and high measurement accuracy without installing on‐site observation instruments. In this paper, we offer coseismic and postseismic crustal deformation maps associated with the 2016 Kumamoto earthquake sequence on Kyushu Island, SW Japan, which were derived using the Japanese SAR satellite ALOS‐2/PALSAR‐2 data. Measurements of displacements along with details on Earth surface ruptures due to earthquakes also allow us to understand the variations in rupture processes along seismogenic faults. Furthermore, comparisons of the spatial and temporal variations of coseismic and postseismic deformations can provide information on the geophysical mechanisms of long‐term deformations following a mainshock. Key Points: PALSAR‐2 pixel tracking and InSAR revealed near‐fault coseismic and postseismic deformations due to the 2016 Kumamoto earthquake sequenceSignificant coseismic displacement variations along the surface ruptures imply complicated fault rupture processes during the mainshockPostseismic deformations can be interpreted by multiple physical processes and geological heterogeneous structures [ABSTRACT FROM AUTHOR]

Coeruptive deformation helps to interpret physical processes associated with volcanic eruptions. Because phreatic eruptions cause small, localized coeruptive deformation, we sometimes fail to identify plausible deformation signals. Satellite synthetic aperture radar (SAR) data allow us to identify extensive deformation fields with high spatial resolutions. Herein, we report coeruptive crustal deformation associated with the 2018 Kusatsu-Shirane phreatic eruption detected by time series analyses of L-band satellite SAR (ALOS-2/PALSAR-2) data. Cumulative deformation maps derived from SAR time series analyses show that subsidence and eastward displacement dominate the southwestern side of an eruptive crater with a spatial extent of approximately 2 km in diameter. Although we were unable to identify any significant deformation signals before the 2018 eruption, posteruptive deformation on the southwestern side of the crater has been ongoing until the end of 2019. This prolonged deformation implies the progression of posteruptive physical processes within a confined hydrothermal system, such as volcanic fluid discharge, similar to the processes observed during the 2014 Ontake eruption. Although accumulated snow and dense vegetation hinder the detection of deformation signals on Kusatsu-Shirane volcano using conventional InSAR data, L-band SAR with various temporal baselines allowed us to successfully extract both coeruptive and posteruptive deformation signals. The extracted cumulative deformation is well explained by a combination of normal faulting with a left-lateral slip component along a southwest-dipping fault plane and an isotropic deflation. Based on the geological background in which the shallow hydrothermal system develops across Kusatsu-Shirane volcano, the inferred dislocation plane can be considered as a degassing pathway from the shallow hydrothermal system to the surface due to the phreatic eruption. We reconfirmed that SAR data are a robust tool for detecting coeruptive and posteruptive deformations, which are helpful for understanding shallow physical processes associated with phreatic eruptions at active volcanoes. [ABSTRACT FROM AUTHOR]

Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of wind speed from SAR wind maps and from 10 meteorological masts, established specifically for wind energy in the study area, are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a root mean square error of 1.17 m s−1, bias of −0.25 m s−1, standard deviation of 1.88 m s−1 and correlation coefficient of R2 0.783. Wind directions from a global atmospheric model, interpolated in time and space, are used as input to the geophysical model function CMOD-5 for SAR wind retrieval. Wind directions compared to mast observations show a root mean square error of 6.29° with a bias of 7.75°, standard deviation of 20.11° and R2 of 0.950. The scale and shape parameters, A and k, respectively, from the Weibull probability density function are compared at only one available mast and the results deviate ~2% for A but ~16% for k. Maps of A and k, and wind power density based on more than 1000 satellite images show wind power density values to range from 300 to 800 W m−2 for the 14 existing and 42 planned wind farms.

A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification. [ABSTRACT FROM AUTHOR]

Persistent scatterer (PS) interferometry (PSI) techniques are designed to measure ground deformations using satellite synthetic aperture radar (SAR) data. They rely on the identification of pixels not severely affected by spatial or temporal decorrelation, which, in general, correspond to pointlike PSs commonly found in urban areas. However, in urban areas, we can find not only PSs but also distributed scatterers (DSs) whose phase information may be exploited for PSI applications. Estimation of DS parameters requires speckle filtering to be applied to the complex SAR data, but conventional speckle filtering approaches tend to mask PS information due to spatial averaging. In the context of single-polarization PSI, adaptive speckle filtering strategies based on the exploitation of amplitude temporal statistics have been proposed, which seek to avoid spatial filtering on nonhomogeneous areas. Given the growing interest on polarimetric PSI techniques, i.e., those using polarimetric diversity to increase performance over conventional single-polarization PSI, in this paper, we propose an adaptive spatial filter driven by polarimetric temporal statistics, rather than single-polarization amplitudes. The proposed approach is able to filter DS while preserving PS information. In addition, a new methodology for the joint processing of PS and DS in the context of PSI is introduced. The technique has been tested for two different urban data sets: 41 dual-polarization TerraSAR-X images of Murcia (Spain) and 31 full-polarization Radarsat-2 images of Barcelona (Spain). Results show an important improvement in terms of number of pixels with valid deformation information, hence denser area coverage. [ABSTRACT FROM PUBLISHER]

Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of wind speed from SAR wind maps and from 10 meteorological masts, established specifically for wind energy in the study area, are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a root mean square error of 1.17 m s-1, bias of -0.25 m s-1, standard deviation of 1.88 m s-1 and correlation coefficient of R2 0.783. Wind directions from a global atmospheric model, interpolated in time and space, are used as input to the geophysical model function CMOD-5 for SAR wind retrieval. Wind directions compared to mast observations show a root mean square error of 6.29° with a bias of 7.75°, standard deviation of 20.11° and R2 of 0.950. The scale and shape parameters, A and k, respectively, from the Weibull probability density function are compared at only one available mast and the results deviate ∼2% for A but ∼16% for k. Maps of A and k, and wind power density based on more than 1000 satellite images show wind power density values to range from 300 to 800 W m-2 for the 14 existing and 42 planned wind farms. [ABSTRACT FROM AUTHOR]

Han, H.G. and Lee, M.J., 2020. A method for classifying land and ocean area by removing sentinel-1 speckle noise. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 33-38. Coconut Creek (Florida), ISSN 0749-0208. In Korea, satellite image-based land cover maps are limited because they are based on time-consuming pixel value-based classification techniques. The main categories of land cover classification are water and land; therefore, synthetic-aperture radar (SAR) images with high water reflectivity may be used to improve land cover map classification accuracy. In this study, C-band SAR images obtained by the Sentinel-1 satellite are used, which include various noises including speckle noise. To remove speckle noise, this paper applied Lee, Gamma, and Frost filters, and found that the Lee filter offered the best performance. By combining a stacking technique and the Lee filter, this paper successfully classified the target water system using image dichotomy and histogram analyses of the region of interest (ROI). The resulting land cover map showed 90 % accuracy compared to the pixel-based map, and comparison with the optical image showed that water coverage was effectively classified. This classification of forest reservoirs, which are difficult to distinguish in optical images, was rated as excellent. Thus, this speckle noise-removal technique will facilitate the improvement of land cover classification accuracy, particularly for flood boundaries and shorelines. [ABSTRACT FROM AUTHOR]