Jeewantinie Kapilaratne | Nagaoka University of Technology (original) (raw)
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Papers by Jeewantinie Kapilaratne
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, May 17, 2022
Single image super resolution (SISR) technology has been attracted much attention from remote sen... more Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.
18th Annual Meeting of the Asia Oceania Geosciences Society
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Flooding is considered as one of the most devastated natural disasters due to its adverse effect ... more Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/ km 2 with a competitive accuracy and minimal system requirements than ResNet101.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. Aftermath of this contemporary issue the natural disasters such as Floods, Drought, Cyclones, Landslides etc. becoming a frequent phenomenon. Storm water floods are more recurrent hazardous events for urban regions in Sri Lanka. Colombo is the preeminent city where people, resources and infrastructure are concentrated. After decentralizing Colombo; suburban residents also suffered not only by urbanization pressure but also the consequences of mindless urbanization. Hence, this study is focused on analysing the effect of land use change for the storm water flooding in Colombo sub urban conditions. Numbers of studies have been carried out in...
Hydrological Research Letters, 2015
Journal of Water Resource and Hydraulic Engineering
Journal of Water Resource and Hydraulic Engineering
An effective temperature correction method for dielectric sensors is important to ensure the accu... more An effective temperature correction method for dielectric sensors is important to ensure the accuracy of soil water content (SWC) measurements of local to regional-scale soil moisture monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at soil moisture monitoring networks with different sensor setups and those that cover diverse climatic conditions and soil types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and soil type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International Soil Moisture Monitoring Network and another nine stations from a local soil moisture monitoring network in Mongolia. Soil moisture monitoring networks used in this study cover four major climates and six major soil types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a significant error factor comparable to ±1% manufacturer's accuracy.
As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used t... more As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used to measure soil water content. Not only in hydrological applications, the measurements are also used as ground truth of satellite remote sensing of soil moisture. However, TDR measurements usually include
diurnal fluctuation caused by diurnal change of temperature. Though this is an old problem, there is not a general solution. The purpose of this study is to develop an algorithm to remove temperature effects of TDR measurements by analyzing its relationship with meteorological variables. From data observed at a Mongolian site, it is found that impact of soil temperature on soil water content is nearly proportional to soil temperature itself and soil water content. An algorithm is developed and applied to the Mongolian data set. The temperature effects can be effectively removed under dry and wet conditions.
KEYWORDS soil water content; temperature effect; time-domain reflectometry; correction; Mongolia
As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used t... more As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used to measure
soil water content. Not only in hydrological applications, the measurements are also used as ground truth of satellite
remote sensing of soil moisture. However, TDR measurements usually include diurnal fluctuation caused by diurnal
change of temperature. Though this is an old problem, there is not a general solution. The purpose of this study
is to develop an algorithm to remove temperature effects of TDR measurements by analyzing its relationship with
meteorological variables. From data observed at a Mongolian site, it is found that impact of temperature on soil water
content is nearly proportional to itself and soil water content. An algorithm is developed and applied to the Mongolian
data set. The temperature effects can are effectively removed under dry and wet conditions.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. Aftermath of this contemporary issue the natural disasters such as Floods, Drought, Cyclones, Landslides etc. becoming a frequent phenomenon. Storm water floods are more recurrent hazardous events for urban regions in Sri Lanka. Colombo is the preeminent city where people, resources and infrastructure are concentrated. After decentralizing Colombo; suburban residents also suffered not only by urbanization pressure but also the consequences of mindless urbanization. Hence, this study is focused on analysing the effect of land use change for the storm water flooding in Colombo sub urban conditions. Numbers of studies have been carried out in this regard; however, severe effects that people are doing to natural environment have not been fully addressed. This study incorporates with the World view_2 panchromatic, multispectral and stereo images. Land use change was identified from the land use maps generated by classified images and where the Support Vector Machine became the most appropriate classification technique for a suburban composition with resulting 80% classification accuracy and more than 0.8 Kappa Coefficient. Marsh lands which identified as major land use type for the flood scenario of the area and significant marsh lands had identified for the analysis. Analysis were carried out regarding mutation of those land cover (2007, 2000, 2004, 1983) with the water level data and meteorological components for the identification of effect of land use change for this dilemma.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting in driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. This has contributed to aggravate natural disasters such as floods, drought, cyclones, landslides etc.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, May 17, 2022
Single image super resolution (SISR) technology has been attracted much attention from remote sen... more Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.
18th Annual Meeting of the Asia Oceania Geosciences Society
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Flooding is considered as one of the most devastated natural disasters due to its adverse effect ... more Flooding is considered as one of the most devastated natural disasters due to its adverse effect on human lives as well as economy. Since more population concentrate towards flood prone areas and frequent occurrence of flood events due to global climate change, there is an urgent need in remote sensing community for faster and reliable inundation mapping technologies to increase the preparedness of population and reduce the catastrophic impact. With the recent advancement in remote sensing technologies and integration capability of deep learning algorithms with remote sensing data makes faster mapping of large area is feasible. Therefore, this study attempted to explore a faster and low cost solution for flood area extraction by integrating convolution neural networks (CNNs) with high resolution (1.5m) SPOT satellite images. By consider the system requirement as a measure of cost, capabilities (speed and accuracy) of a deeper (ResNet101) and a shallower (MobileNetV2) CNNs on flood mapping were examined and compared. The models were trained and tested with satellite images captured during several flood events occurred in Japan. It is observed from the results that ResNet101 obtained better flood area mapping accuracy than MobileNetV2. Whereas, MobileNetV2 is having much higher capabilities in faster mapping in 0.3 s/ km 2 with a competitive accuracy and minimal system requirements than ResNet101.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. Aftermath of this contemporary issue the natural disasters such as Floods, Drought, Cyclones, Landslides etc. becoming a frequent phenomenon. Storm water floods are more recurrent hazardous events for urban regions in Sri Lanka. Colombo is the preeminent city where people, resources and infrastructure are concentrated. After decentralizing Colombo; suburban residents also suffered not only by urbanization pressure but also the consequences of mindless urbanization. Hence, this study is focused on analysing the effect of land use change for the storm water flooding in Colombo sub urban conditions. Numbers of studies have been carried out in...
Hydrological Research Letters, 2015
Journal of Water Resource and Hydraulic Engineering
Journal of Water Resource and Hydraulic Engineering
An effective temperature correction method for dielectric sensors is important to ensure the accu... more An effective temperature correction method for dielectric sensors is important to ensure the accuracy of soil water content (SWC) measurements of local to regional-scale soil moisture monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at soil moisture monitoring networks with different sensor setups and those that cover diverse climatic conditions and soil types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and soil type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International Soil Moisture Monitoring Network and another nine stations from a local soil moisture monitoring network in Mongolia. Soil moisture monitoring networks used in this study cover four major climates and six major soil types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a significant error factor comparable to ±1% manufacturer's accuracy.
As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used t... more As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used to measure soil water content. Not only in hydrological applications, the measurements are also used as ground truth of satellite remote sensing of soil moisture. However, TDR measurements usually include
diurnal fluctuation caused by diurnal change of temperature. Though this is an old problem, there is not a general solution. The purpose of this study is to develop an algorithm to remove temperature effects of TDR measurements by analyzing its relationship with meteorological variables. From data observed at a Mongolian site, it is found that impact of soil temperature on soil water content is nearly proportional to soil temperature itself and soil water content. An algorithm is developed and applied to the Mongolian data set. The temperature effects can be effectively removed under dry and wet conditions.
KEYWORDS soil water content; temperature effect; time-domain reflectometry; correction; Mongolia
As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used t... more As a convenient, easy-to-use tool, time-domain reflectometry (TDR) is becoming extensively used to measure
soil water content. Not only in hydrological applications, the measurements are also used as ground truth of satellite
remote sensing of soil moisture. However, TDR measurements usually include diurnal fluctuation caused by diurnal
change of temperature. Though this is an old problem, there is not a general solution. The purpose of this study
is to develop an algorithm to remove temperature effects of TDR measurements by analyzing its relationship with
meteorological variables. From data observed at a Mongolian site, it is found that impact of temperature on soil water
content is nearly proportional to itself and soil water content. An algorithm is developed and applied to the Mongolian
data set. The temperature effects can are effectively removed under dry and wet conditions.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. Aftermath of this contemporary issue the natural disasters such as Floods, Drought, Cyclones, Landslides etc. becoming a frequent phenomenon. Storm water floods are more recurrent hazardous events for urban regions in Sri Lanka. Colombo is the preeminent city where people, resources and infrastructure are concentrated. After decentralizing Colombo; suburban residents also suffered not only by urbanization pressure but also the consequences of mindless urbanization. Hence, this study is focused on analysing the effect of land use change for the storm water flooding in Colombo sub urban conditions. Numbers of studies have been carried out in this regard; however, severe effects that people are doing to natural environment have not been fully addressed. This study incorporates with the World view_2 panchromatic, multispectral and stereo images. Land use change was identified from the land use maps generated by classified images and where the Support Vector Machine became the most appropriate classification technique for a suburban composition with resulting 80% classification accuracy and more than 0.8 Kappa Coefficient. Marsh lands which identified as major land use type for the flood scenario of the area and significant marsh lands had identified for the analysis. Analysis were carried out regarding mutation of those land cover (2007, 2000, 2004, 1983) with the water level data and meteorological components for the identification of effect of land use change for this dilemma.
All living beings modify their environment. Humans are no exception; they have modified lands to ... more All living beings modify their environment. Humans are no exception; they have modified lands to obtain food and other needs for thousands of years. Present rates, extents and intensities of Land Use Change (LUC) are far greater, resulting in driving unprecedented changes in ecosystems and environmental processes at local, regional and global scales. This has contributed to aggravate natural disasters such as floods, drought, cyclones, landslides etc.