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Although deep neural networks hold the state-of-the-art in several remote sensing tasks, their black-box operation hinders the understanding of their decisions, concealing any bias and other shortcomings in datasets and model performance. To this end, we have applied explainable artificial intelligence (XAI) methods in remote sensing multi-label classification tasks towards producing human-interpretable explanations and improve transparency. In particular, we utilized and trained deep learning models with state-of-the-art performance in the benchmark BigEarthNet and SEN12MS datasets. Ten XAI methods were employed towards understanding and interpreting models' predictions, along with quantitative metrics to assess and compare their performance. Numerous experiments were performed to assess the overall performance of XAI methods for straightforward prediction cases, competing multiple labels, as well as misclassification cases. According to our findings, Occlusion, Grad-CAM and Lime were the most interpretable and reliable XAI methods. However, none delivers high-resolution outputs, while apart from Grad-CAM, both Lime and Occlusion are computationally expensive. We also highlight different aspects of XAI performance and elaborate with insights on black-box decisions in order to improve transparency, understand their behavior and reveal, as well, datasets’ particularities.
Ioannis Kakogeorgiou; Konstantinos Karantzalos. Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation 2021, 103, 102520 .
AMA StyleIoannis Kakogeorgiou, Konstantinos Karantzalos. Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing. International Journal of Applied Earth Observation and Geoinformation. 2021; 103 ():102520.
Chicago/Turabian StyleIoannis Kakogeorgiou; Konstantinos Karantzalos. 2021. "Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing." International Journal of Applied Earth Observation and Geoinformation 103, no. : 102520.
MARine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task. The dataset contains: i. 1381 patches (256 x 256) structured by Unique Dates and S2 Tiles. Each patch is provided along with the corresponding masks of pixel-level annotated classes (*_cl) and confidence levels (*_conf). Patches are given in GeoTiff format. ii. Shapefiles data in WGS’84/ UTM projection, with file naming convention following the scheme: s2_dd-mm-yy_ttt, where s2 denotes the S2 sensor, dd denotes the day, mm the month, yy the year and ttt denotes the S2 tile. Shapefiles include the class of each annotation along with the confidence level and the marine debris report description. iii. Train, Validation and Test split for evaluating machine learning algorithms. iv. The assigned multi-labels for each path (labels_mapping.txt). The mapping between Digital Numbers and Classes is: 1: Marine Debris 2: Dense Sargassum 3: Sparse Sargassum 4: Natural Organic Material 5: Ship 6: Clouds 7: Marine Water 8: Sediment-Laden Water 9: Foam 10: Turbid Water 11: Shallow Water 12: Waves 13: Cloud Shadows 14: Wakes 15: Mixed Water The mapping between Digital Numbers and Confidence level is: 1: High 2: Moderate 3: Low The mapping between Digital Numbers and marine debris Report existence is: 1: Very close 2: Away 3: No For the quick start guide visit marine-debris.github.io
Katerina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. MARIDA: Marine Debris Archive. 2021, 1 .
AMA StyleKaterina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Konstantinos Karantzalos. MARIDA: Marine Debris Archive. . 2021; ():1.
Chicago/Turabian StyleKaterina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. 2021. "MARIDA: Marine Debris Archive." , no. : 1.
Quick Start Guide for MARIDA (Marine Debris Archive)
Katerina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. marine-debris/marine-debris.github.io: MARIDA V1.0.0. 2021, 1 .
AMA StyleKaterina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Konstantinos Karantzalos. marine-debris/marine-debris.github.io: MARIDA V1.0.0. . 2021; ():1.
Chicago/Turabian StyleKaterina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. 2021. "marine-debris/marine-debris.github.io: MARIDA V1.0.0." , no. : 1.
MARine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task. The dataset contains: i. 1381 patches (256 x 256) structured by Unique Dates and S2 Tiles. Each patch is provided along with the corresponding masks of pixel-level annotated classes (*_cl) and confidence levels (*_conf). Patches are given in GeoTiff format. ii. Shapefiles data in WGS’84/ UTM projection, with file naming convention following the scheme: s2_dd-mm-yy_ttt, where s2 denotes the S2 sensor, dd denotes the day, mm the month, yy the year and ttt denotes the S2 tile. Shapefiles include the class of each annotation along with the confidence level and the marine debris report description. iii. Train, Validation and Test split for evaluating machine learning algorithms. iv. The assigned multi-labels for each path (labels_mapping.txt). The mapping between Digital Numbers and Classes is: 1: Marine Debris 2: Dense Sargassum 3: Sparse Sargassum 4: Natural Organic Material 5: Ship 6: Clouds 7: Marine Water 8: Sediment-Laden Water 9: Foam 10: Turbid Water 11: Shallow Water 12: Waves 13: Cloud Shadows 14: Wakes 15: Mixed Water The mapping between Digital Numbers and Confidence level is: 1: High 2: Moderate 3: Low The mapping between Digital Numbers and marine debris Report existence is: 1: Very close 2: Away 3: No For the quick start guide visit marine-debris.github.io
Katerina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. MARIDA: Marine Debris Archive. 2021, 1 .
AMA StyleKaterina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Konstantinos Karantzalos. MARIDA: Marine Debris Archive. . 2021; ():1.
Chicago/Turabian StyleKaterina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. 2021. "MARIDA: Marine Debris Archive." , no. : 1.
Quick Start Guide for MARIDA (Marine Debris Archive)
Katerina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. marine-debris/marine-debris.github.io: MARIDA V1.0.0. 2021, 1 .
AMA StyleKaterina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Konstantinos Karantzalos. marine-debris/marine-debris.github.io: MARIDA V1.0.0. . 2021; ():1.
Chicago/Turabian StyleKaterina Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Konstantinos Karantzalos. 2021. "marine-debris/marine-debris.github.io: MARIDA V1.0.0." , no. : 1.
In this work, we elaborate on the gained insights from various classification experiments towards detailed land cover mapping over four representative regions of different environmental characteristics in Greece. In particular, the proposed methodology exploits Sentinel-2 data at an annual basis, for the joint classification of 35 land cover and crop type classes. A number of pre-processing steps were employed on the satellite data, in order to address atmospheric and geometric effects, as well as clouds and pertinent shadows. Several classification set-ups were designed and performed using either time series of spectral features or temporal features. The latter consisted of statistical metrics, derived from the spectral time series, and therefore were significantly reduced in dimension. Experiments using the Random Forest algorithm were performed by building several per-tile models, as well as cross- regional models based on training data from all considered regions/tiles. Overall classification accuracy rates exceeded 90% for most experiments. Further analysis on the experimental results highlighted that crop types were classified more accurately when using the spectral time series features, compared to the temporal ones. Classification accuracy for non-crop classes proved much less affected by the type of employed features. The inclusion of auxiliary data layers was beneficial in all cases, both for overall and for per-class accuracy metrics. Qualitative evaluation on the predicted maps further affirmed the efficiency of the developed methodology.
C. Karakizi; Z. Kandylakis; A. D. Vaiopoulos; K. Karantzalos. JOINT LAND COVER AND CROP TYPE MAPPING USING MULTI-TEMPORAL SENTINEL-2 DATA FROM VARIOUS ENVIRONMENTAL ZONES IN GREECE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2021, XLIII-B3-2, 319 -326.
AMA StyleC. Karakizi, Z. Kandylakis, A. D. Vaiopoulos, K. Karantzalos. JOINT LAND COVER AND CROP TYPE MAPPING USING MULTI-TEMPORAL SENTINEL-2 DATA FROM VARIOUS ENVIRONMENTAL ZONES IN GREECE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2021; XLIII-B3-2 ():319-326.
Chicago/Turabian StyleC. Karakizi; Z. Kandylakis; A. D. Vaiopoulos; K. Karantzalos. 2021. "JOINT LAND COVER AND CROP TYPE MAPPING USING MULTI-TEMPORAL SENTINEL-2 DATA FROM VARIOUS ENVIRONMENTAL ZONES IN GREECE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2, no. : 319-326.
This workflow corresponds to the study "Global Assessment of Innovative Solutions to tackle Marine Litter" by Bellou, Nikoleta; Gambardella, Chiara; Karantzalos, Konstantinos; Monteiro, João; Canning-Close, João; Kemna, Stephanie; Arrieta-Giron, Camilo A.; Lemmen, Carsten, published in Nature Sustainability 2021, doi:10.1038/s41893-021-00726-2 Please cite the original publication when using this software. An automated data workflow for the analysis and graphical representation of the data was conducted with open source software, among them Python, Seaborn, LaTeX, and Inkscape.
Nikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Monteiro; João Canning-Code; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter. 2021, 1 .
AMA StyleNikoleta Bellou, Chiara Gambardella, Konstantinos Karantzalos, João Monteiro, João Canning-Code, Stephanie Kemna, Camilo A. Arrieta-Giron, Carsten Lemmen. Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter. . 2021; ():1.
Chicago/Turabian StyleNikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Monteiro; João Canning-Code; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. 2021. "Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter." , no. : 1.
This workflow corresponds to the study "Global Assessment of Innovative Solutions to tackle Marine Litter" by Bellou, Nikoleta; Gambardella, Chiara; Karantzalos, Konstantinos; Monteiro, João; Canning-Close, João; Kemna, Stephanie; Arrieta-Giron, Camilo A.; Lemmen, Carsten, published in Nature Sustainability 2021, doi:10.1038/s41893-021-00726-2 Please cite the original publication when using this software. An automated data workflow for the analysis and graphical representation of the data was conducted with open source software, among them Python, Seaborn, LaTeX, and Inkscape.
Nikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Monteiro; João Canning-Code; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter. 2021, 1 .
AMA StyleNikoleta Bellou, Chiara Gambardella, Konstantinos Karantzalos, João Monteiro, João Canning-Code, Stephanie Kemna, Camilo A. Arrieta-Giron, Carsten Lemmen. Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter. . 2021; ():1.
Chicago/Turabian StyleNikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Monteiro; João Canning-Code; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. 2021. "Data analysis for figure creation: Global Assessment of Innovative Solutions to tackle Marine Litter." , no. : 1.
Marine litter is one of the most relevant pollution problems that our oceans are facing today. Marine litter in our oceans is a major threat to a sustainable planet. Here, we provide a comprehensive analysis of cutting-edge solutions developed globally to prevent, monitor and clean marine litter. Prevention in this research includes only innovative solutions to prevent litter entering oceans and seas rather than interventions such as waste reduction and recycling. On the basis of extensive search and data compilation, our analysis reveals that information is dispersed across platforms and is not easily accessible. In total, 177 solutions—the equivalent to <0.9% of the search hits—fulfilled our validation criteria and were evaluated. Most solutions (n = 106, 60%) primarily address monitoring and were developed during the past 3 years, with the scientific community being the key driver. Few solutions reached mature technical readiness and market availability, while none were validated for efficiency and environmental impact. Looking ahead, we elaborate on the limitations of the existing solutions, the challenges of developing new solutions, and provide recommendations for funding schemes and policy instruments to prevent, monitor and clean marine litter globally. In doing so, we encourage researchers, innovators and policy-makers worldwide to act towards achieving and sustaining a cleaner ocean for future generations.
Nikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Gama Monteiro; João Canning-Clode; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. Global assessment of innovative solutions to tackle marine litter. Nature Sustainability 2021, 4, 516 -524.
AMA StyleNikoleta Bellou, Chiara Gambardella, Konstantinos Karantzalos, João Gama Monteiro, João Canning-Clode, Stephanie Kemna, Camilo A. Arrieta-Giron, Carsten Lemmen. Global assessment of innovative solutions to tackle marine litter. Nature Sustainability. 2021; 4 (6):516-524.
Chicago/Turabian StyleNikoleta Bellou; Chiara Gambardella; Konstantinos Karantzalos; João Gama Monteiro; João Canning-Clode; Stephanie Kemna; Camilo A. Arrieta-Giron; Carsten Lemmen. 2021. "Global assessment of innovative solutions to tackle marine litter." Nature Sustainability 4, no. 6: 516-524.
The increasing need for accurate bathymetric mapping is essential for a plethora of offshore activities. Even though aerial image datasets through Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques can provide a low-cost alternative compared to LiDAR and SONAR, offering additionally, important visual information, water refraction poses significant obstacles in delivering accurate bathymetry. In this article, the generation of manned and unmanned airborne synthetic datasets of dry and water covered areas is presented. These data are used to train models for correcting the geometric effects of refraction on real-world image-based point clouds and aerial images. Based on a thorough evaluation, important improvements are presented, indicating the increased accuracy and the reduced noise in the point clouds of the derived bathymetric products, meeting also the International Hydrographic Organization’s (IHO) standards.
Panagiotis Agrafiotis; Konstantinos Karantzalos; Andreas Georgopoulos; Dimitrios Skarlatos. Learning from Synthetic Data: Enhancing Refraction Correction Accuracy for Airborne Image-Based Bathymetric Mapping of Shallow Coastal Waters. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2021, 1 -19.
AMA StylePanagiotis Agrafiotis, Konstantinos Karantzalos, Andreas Georgopoulos, Dimitrios Skarlatos. Learning from Synthetic Data: Enhancing Refraction Correction Accuracy for Airborne Image-Based Bathymetric Mapping of Shallow Coastal Waters. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2021; ():1-19.
Chicago/Turabian StylePanagiotis Agrafiotis; Konstantinos Karantzalos; Andreas Georgopoulos; Dimitrios Skarlatos. 2021. "Learning from Synthetic Data: Enhancing Refraction Correction Accuracy for Airborne Image-Based Bathymetric Mapping of Shallow Coastal Waters." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science , no. : 1-19.
Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method.
Maria Papadomanolaki; Stergios Christodoulidis; Konstantinos Karantzalos; Maria Vakalopoulou. Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning. Remote Sensing 2021, 13, 1294 .
AMA StyleMaria Papadomanolaki, Stergios Christodoulidis, Konstantinos Karantzalos, Maria Vakalopoulou. Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning. Remote Sensing. 2021; 13 (7):1294.
Chicago/Turabian StyleMaria Papadomanolaki; Stergios Christodoulidis; Konstantinos Karantzalos; Maria Vakalopoulou. 2021. "Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning." Remote Sensing 13, no. 7: 1294.
Plastic debris in the global ocean is considered an essential issue with severe implications for human health and marine ecosystems. Remote sensing is a useful tool for detecting and identifying marine pollution; however, there are still few studies and benchmark datasets for developing monitoring solutions for marine plastic debris detection from high-resolution satellite data.
Here, we present an annotated plastic debris dataset from different geographical regions, seasons, and years, including annotations for sea surface features (e.g., foam), objects (e.g., ship) and floating macroalgae species such as Sargassum. Our dataset is based on high-resolution multispectral satellite observations collected mainly for the period 2014-2020 over the Gulf of Honduras (Caribbean Sea). Over this region, large plastic debris masses and Sargassum macroalgae blooms have been frequently reported, suggesting that it is an ideal region to examine satellite sensors' effectiveness in plastic debris identification, as well as monitoring along with sea surface circulation and meteorological data.
We also present a set of machine learning classification frameworks for marine debris detection on high-resolution satellite imagery, comparing qualitatively and quantitatively their overall performance. The new algorithms were validated against different regions that have been reported as major plastic polluted areas, as well as their performance was compared to well-established FAI and new promising FDI. This benchmark study can trigger more research and developement efforts towards the systematic detection and monitoring of marine plastic pollution.
Aikaterini Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Dionysios E. Raitsos; Konstantinos Karantzalos. Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data. 2021, 1 .
AMA StyleAikaterini Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, Konstantinos Karantzalos. Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data. . 2021; ():1.
Chicago/Turabian StyleAikaterini Kikaki; Ioannis Kakogeorgiou; Paraskevi Mikeli; Dionysios E. Raitsos; Konstantinos Karantzalos. 2021. "Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data." , no. : 1.
In this article, we present a deep multitask learning framework able to couple semantic segmentation and change detection using fully convolutional long short-term memory (LSTM) networks. In particular, we present a UNet-like architecture (L-UNet) that models the temporal relationship of spatial feature representations using integrated fully convolutional LSTM blocks on top of every encoding level. In this way, the network is able to capture the temporal relationship of spatial feature vectors in all encoding levels without the need to downsample or flatten them, forming an end-to-end trainable framework. Moreover, we further enrich the L-UNet architecture with an additional decoding branch that performs semantic segmentation on the available semantic categories that are presented in the different input dates, forming a multitask framework. Different loss quantities are also defined and combined together in a circular way to boost the overall performance. The developed methodology has been evaluated on three different data sets, i.e., the challenging bitemporal high-resolution Office National d'Etudes et de Recherches Aérospatiales (ONERA) Satellite Change Detection (OSCD) Sentinel-2 data set, the very high-resolution (VHR) multitemporal data set of the East Prefecture of Attica, Greece, and finally, the multitemporal VHR SpaceNet7 data set. Promising quantitative and qualitative results demonstrated that the synergy among the tasks can boost up the achieved performances. In particular, the proposed multitask framework contributed to a significant decrease in false-positive detections, with the F1 rate outperforming other state-of-the-art methods by at least 2.1% and 4.9% in the Attica VHR and SpaceNet7 data set cases, respectively. Our models and code can be found at https://github.com/mpapadomanolaki/multi-task-L-UNet.
Maria Papadomanolaki; Maria Vakalopoulou; Konstantinos Karantzalos. A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection. IEEE Transactions on Geoscience and Remote Sensing 2021, 59, 7651 -7668.
AMA StyleMaria Papadomanolaki, Maria Vakalopoulou, Konstantinos Karantzalos. A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 2021; 59 (9):7651-7668.
Chicago/Turabian StyleMaria Papadomanolaki; Maria Vakalopoulou; Konstantinos Karantzalos. 2021. "A Deep Multitask Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection." IEEE Transactions on Geoscience and Remote Sensing 59, no. 9: 7651-7668.
Freely available satellite image time-series are currently the most exploited data towards land cover mapping. In this work we assess the contribution of spectral and temporal features for the detailed, i.e., with more than thirty classes, land cover and crop type mapping based on annual Sentinel-2 data. As a baseline we employed a datacube consisting of spectral features, i.e., spectral bands and indices from one tile of Sentinel-2A data for the year 2016. Then we formed two different datacubes of reduced dimensions, containing either spectrotemporal or temporal features and performed the same experiments in order to assess their contribution. For the second dataset only spectral features that fulfilled certain temporal conditions were retained, reducing by 40% the initial datacube dimensionality. The third dataset was formed only of temporal features resulting to a reduction of 50%. A random forest classifier was employed for the classification procedure and standard accuracy metrics for the validation. All experiments resulted into high overall accuracy rates of over 90% while rates for average F-score metric exceeded 78% in all cases. The quantitative and qualitative validation indicated that the baseline dataset modestly outperformed the other two of spectrotemporal and temporal features. Insights regarding the influence of spectral differentiation among classes and the impact of their sample size, on the per-class performance are further discussed. The importance of spatial independency for training and testing sets was also demonstrated highlighting the need of following best practises during validation in order to deliver a realistic estimation of the produced map accuracy.
C. Karakizi; I. A. Tsiotas; Z. Kandylakis; A. Vaiopoulos; K. Karantzalos. ASSESSING THE CONTRIBUTION OF SPECTRAL AND TEMPORAL FEATURES FOR ANNUAL LAND COVER AND CROP TYPE MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLIII-B3-2, 1555 -1562.
AMA StyleC. Karakizi, I. A. Tsiotas, Z. Kandylakis, A. Vaiopoulos, K. Karantzalos. ASSESSING THE CONTRIBUTION OF SPECTRAL AND TEMPORAL FEATURES FOR ANNUAL LAND COVER AND CROP TYPE MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLIII-B3-2 ():1555-1562.
Chicago/Turabian StyleC. Karakizi; I. A. Tsiotas; Z. Kandylakis; A. Vaiopoulos; K. Karantzalos. 2020. "ASSESSING THE CONTRIBUTION OF SPECTRAL AND TEMPORAL FEATURES FOR ANNUAL LAND COVER AND CROP TYPE MAPPING." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2, no. : 1555-1562.
Recent technological advances in the underwater sensing instrumentation provide currently active multibeam echosounders that can acquire backscatter observations from multiple spectral frequencies. In this paper, the main objective was to design, develop and validate an efficient and robust multispectral, multibeam data processing framework including advanced machine learning tools for seabed classification. In order to do so, we have integrated different machine learning tools like support vector machines and random forests towards the classification of seabed classes. We have performed extensive experiments with different splitting ratios, regarding training and testing sets, in order to assess possible overfitting. The entire pipeline has been implemented in a scalable containerized manner in order to be deployed in cloud infrastructures and more specifically at the European Open Science Cloud. Experimental results, the performed qualitative and quantitative evaluation along with the comparison with the state of the art indicated the quite promising potential of our approach.
P. Mertikas; K. Karantzalos. SEAFLOOR MAPPING FROM MULTISPECTRAL MULTIBEAM ACOUSTIC DATA AT THE EUROPEAN OPEN SCIENCE CLOUD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLIII-B2-2, 985 -990.
AMA StyleP. Mertikas, K. Karantzalos. SEAFLOOR MAPPING FROM MULTISPECTRAL MULTIBEAM ACOUSTIC DATA AT THE EUROPEAN OPEN SCIENCE CLOUD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLIII-B2-2 ():985-990.
Chicago/Turabian StyleP. Mertikas; K. Karantzalos. 2020. "SEAFLOOR MAPPING FROM MULTISPECTRAL MULTIBEAM ACOUSTIC DATA AT THE EUROPEAN OPEN SCIENCE CLOUD." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2, no. : 985-990.
Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) data is presented, towards the estimation of water stress in vineyards at canopy scale for entire parcels. In particular, aerial broadband spectral data were collected from an integrated SWIR and multispectral instrumentation, onboard an unmanned aerial vehicle (UAV). Concurrently, in-situ leaf stomatal conductance measurements and supplementary data for radiometric and geometric corrections were acquired. A processing pipeline has been designed, developed, and validated, able to execute the required analysis, including data pre-processing, data co-registration, reflectance calibration, canopy extraction and water stress estimation. Experiments were performed at two viticultural regions in Greece, for several vine parcels of four different vine varieties, Sauvignon Blanc, Merlot, Syrah and Xinomavro. The performed qualitative and quantitative assessment indicated that a single model for the estimation of water stress across all studied vine varieties was not able to be established (r2 < 0.30). Relatively high correlation rates (r2 > 0.80) were achieved per variety and per individual variety clone. The overall root mean square error (RMSE) for the estimated canopy water stress was less than 29 mmol m−2 s−1, spanning from no-stress to severe canopy stress levels. Overall, experimental results and validation indicated the quite high potentials of the proposed instrumentation and methodology.
Zacharias Kandylakis; Alexandros Falagas; Christina Karakizi; Konstantinos Karantzalos. Water Stress Estimation in Vineyards from Aerial SWIR and Multispectral UAV Data. Remote Sensing 2020, 12, 2499 .
AMA StyleZacharias Kandylakis, Alexandros Falagas, Christina Karakizi, Konstantinos Karantzalos. Water Stress Estimation in Vineyards from Aerial SWIR and Multispectral UAV Data. Remote Sensing. 2020; 12 (15):2499.
Chicago/Turabian StyleZacharias Kandylakis; Alexandros Falagas; Christina Karakizi; Konstantinos Karantzalos. 2020. "Water Stress Estimation in Vineyards from Aerial SWIR and Multispectral UAV Data." Remote Sensing 12, no. 15: 2499.
Change detection is a very important problem for the remote sensing community. Among the several approaches proposed during recent years, deep learning provides methods and tools that achieve state of the art performances. In this paper, we tackle the problem of urban change detection by constructing a fully convolutional multi-task deep architecture. We present a framework based on the UNet model, with fully convolutional LSTM blocks integrated on top of every encoding level capturing in this way the temporal dynamics of spatial feature representations at different resolution levels. The proposed network is modular due to shared weights which allow the exploitation of multiple (more than two) dates simultaneously. Moreover, our framework provides building segmentation maps by employing a multi-task scheme which extracts additional feature attributes that can reduce the number of false positive pixels. We performed extensive experiments comparing our method with other state of the art approaches using very high resolution images of urban areas. Quantitative and qualitative results reveal the great potential of the proposed scheme, with F1 score outperforming the other compared methods by almost 2.2%.
M. Papadomanolaki; M. Vakalopoulou; K. Karantzalos. URBAN CHANGE DETECTION BASED ON SEMANTIC SEGMENTATION AND FULLY CONVOLUTIONAL LSTM NETWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, V-2-2020, 541 -547.
AMA StyleM. Papadomanolaki, M. Vakalopoulou, K. Karantzalos. URBAN CHANGE DETECTION BASED ON SEMANTIC SEGMENTATION AND FULLY CONVOLUTIONAL LSTM NETWORKS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; V-2-2020 ():541-547.
Chicago/Turabian StyleM. Papadomanolaki; M. Vakalopoulou; K. Karantzalos. 2020. "URBAN CHANGE DETECTION BASED ON SEMANTIC SEGMENTATION AND FULLY CONVOLUTIONAL LSTM NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020, no. : 541-547.
Plastic debris in the global ocean is considered an important issue with severe implications for human health and marine ecosystems. Here, we exploited high-resolution multispectral satellite observations over the Bay Islands and Gulf of Honduras, for the period 2014-2019, to investigate the capability of satellite sensors in detecting marine plastic debris. We verified findings with in situ data, recorded the spectral characteristics of floating plastic litter, and identified plastic debris trajectories and sources. The results showed that plastic debris originating from Guatemala’s and Honduras’ rivers (such as Motagua, Ulua, Cangrejal, Tinto and Aguan) ends up in the Caribbean Sea, mainly during the period of August to March, which includes the main rainfall season. The detected spatial trajectories indicated that floating plastic debris travels with an average speed of 6 km d−1, following primarily a southwest (SW) to northeast (NE) direction, driven by the prevailing sea surface currents. Based on several satellite observations, there is no indication of a specific accumulation point, since plastic debris is dispersed by the dynamic circulation in the broader region. Our findings provide evidence that satellite remote sensing is a valuable, cost-effective tool for monitoring the sources and pathways of plastic debris in marine ecosystems, and thus could eventually support management strategies in the global ocean.
Aikaterini Kikaki; Konstantinos Karantzalos; Caroline A. Power; Dionysios E. Raitsos. Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea). Remote Sensing 2020, 12, 1727 .
AMA StyleAikaterini Kikaki, Konstantinos Karantzalos, Caroline A. Power, Dionysios E. Raitsos. Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea). Remote Sensing. 2020; 12 (11):1727.
Chicago/Turabian StyleAikaterini Kikaki; Konstantinos Karantzalos; Caroline A. Power; Dionysios E. Raitsos. 2020. "Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)." Remote Sensing 12, no. 11: 1727.
Although aerial image-based bathymetric mapping can provide, unlike acoustic or LiDAR (Light Detection and Ranging) sensors, both water depth and visual information, water refraction poses significant challenges for accurate depth estimation. In order to tackle this challenge, we propose an image correction methodology, which first exploits recent machine learning procedures that recover depth from image-based dense point clouds and then corrects refraction on the original imaging dataset. This way, the structure from motion (SfM) and multi-view stereo (MVS) processing pipelines are executed on a refraction-free set of aerial datasets, resulting in highly accurate bathymetric maps. Performed experiments and validation were based on datasets acquired during optimal sea state conditions and derived from four different test-sites characterized by excellent sea bottom visibility and textured seabed. Results demonstrated the high potential of our approach, both in terms of bathymetric accuracy, as well as texture and orthoimage quality.
Panagiotis Agrafiotis; Konstantinos Karantzalos; Andreas Georgopoulos; Dimitrios Skarlatos. Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters. Remote Sensing 2020, 12, 322 .
AMA StylePanagiotis Agrafiotis, Konstantinos Karantzalos, Andreas Georgopoulos, Dimitrios Skarlatos. Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters. Remote Sensing. 2020; 12 (2):322.
Chicago/Turabian StylePanagiotis Agrafiotis; Konstantinos Karantzalos; Andreas Georgopoulos; Dimitrios Skarlatos. 2020. "Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters." Remote Sensing 12, no. 2: 322.
3D semantic segmentation is the joint task of partitioning a point cloud into semantically consistent 3D regions and assigning them to a semantic class/label. While the traditional approaches for 3D semantic segmentation typically rely only on structural information of the objects (i.e. object geometry and shape), the last years many techniques combining both visual and geometric features have emerged, taking advantage of the progress in SfM/MVS algorithms that reconstruct point clouds from multiple overlapping images. Our work describes a hybrid methodology for 3D semantic segmentation, relying both on 2D and 3D space and aiming at exploring whether image selection is critical as regards the accuracy of 3D semantic segmentation of point clouds. Experimental results are demonstrated on a free online dataset depicting city blocks around Paris. The experimental procedure not only validates that hybrid features (geometric and visual) can achieve a more accurate semantic segmentation, but also demonstrates the importance of the most appropriate view for the 2D feature extraction.
A. Adam; L. Grammatikopoulos; G. Karras; E. Protopapadakis; K. Karantzalos. A SEMANTIC 3D POINT CLOUD SEGMENTATION APPROACH BASED ON OPTIMAL VIEW SELECTION FOR 2D IMAGE FEATURE EXTRACTION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-2/W17, 9 -14.
AMA StyleA. Adam, L. Grammatikopoulos, G. Karras, E. Protopapadakis, K. Karantzalos. A SEMANTIC 3D POINT CLOUD SEGMENTATION APPROACH BASED ON OPTIMAL VIEW SELECTION FOR 2D IMAGE FEATURE EXTRACTION. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-2/W17 ():9-14.
Chicago/Turabian StyleA. Adam; L. Grammatikopoulos; G. Karras; E. Protopapadakis; K. Karantzalos. 2019. "A SEMANTIC 3D POINT CLOUD SEGMENTATION APPROACH BASED ON OPTIMAL VIEW SELECTION FOR 2D IMAGE FEATURE EXTRACTION." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17, no. : 9-14.