This paper provides an overview of existing literature on vessel/ship detection and classification from optical satellite imagery. Although SAR (Synthetic Aperture Radar) is still the leading ...technology for maritime monitoring, the number of studies based on optical satellite data is quickly growing. Altogether we analysed 119 papers on optical vessel detection and classification for the period from 1978 to March 2017. We start by introducing all the existing sensor systems for vessel detection, but subsequently focus only on optical imaging satellites. The article demonstrates the temporal development of optical satellite characteristics and connects this to the number and frequency of publications on vessel detection. After presenting the methods used for optical imagery-based vessel detection and classification in detail, along with the achieved detection accuracies, we also report possibilities for fusing optical data with other data sources. The studied papers show that the most common factors greatly influencing the vessel detection accuracy are the following: different weather conditions affecting sea surface characteristics, the quantity of clouds and haze, solar angle, and imaging sensor characteristics. All these factors bring great variations in the selection of the most suitable method; some still continue to pose unsolved challenges. For higher relevance and wider usage, we suggest that the algorithms for detection and classification should support a variety of targets and meteorological conditions, and ideally also a variety of optical satellite sensors. At least, they should be tested on many images under different conditions. This is not usually the case in the existent literature. We also observed that many authors omit an appropriate performance quantification, which is critical for a practical assessment and a numerical comparison of the presented algorithms. Overall it can be seen that vessel monitoring from spaceborne optical images is a popular research topic and has a great operational potential in the near future due to the large amount of satellite data, much of it free and open.
•A review of 119 papers on ship detection and classification from optical satellite.•From 1978 to March 2017, showing an exponential growth in the number of papers.•Most published methods have very limited validation.•While big steps have been made, automatic algorithms are still far from perfect.•Increase in new observation and processing capabilities promises rapid advances.
Urban heat island (UHI) is a phenomenon of high spatial and temporal variabilities. It can develop during night or daytime. UHI monitoring is possible through thermal satellite remote sensing of land ...surface temperature (LST). LST over large areas (size of a city) can be measured at high temporal resolution merely from instruments on-board geostationary satellites. These can cover the diurnal cycle as they provide data even every 5
min (SEVIRI rapid scanning). Sensors on-board the geostationary satellites have, however, poor spatial resolution. Using high spatial resolution is in many regions most important because LST is a spatially inhomogeneous parameter especially in urban areas. UHI characteristics are correlated with the land cover and micro-relief parameters. These are often available in a higher spatial resolution (e.g. NDVI and EVI). Thus, we employed them to enhance the spatial resolution of the SEVIRI LST over central Europe – using moving window analysis – to 1000
m spatial resolution and temporal resolution of 15
min. For each SEVIRI pixel a multiple regression was run on the low resolution data. Regression equation was then used on the high resolution data in order to estimate LST of high spatial and temporal resolutions. The validation over urban areas showed that the downscaled SEVIRI LST is comparable with the MODIS LST with an average root mean square error of 2.5
K. The obtained results make possible to analyse the diurnal cycle of UHI.
► We downscale SEVIRI LST over urban areas. ► LST downscaling is based on principal components of various data. ► The results have 1000
m spatial and 15
min temporal resolutions. ► The validation with MODIS resulted into R
2 of 0.97 and RMSE of 2.5
K.
Alpine topography is formed by a complex series of geomorphological processes that result in a vast number of different landforms. The youngest and most diverse landforms are various Quaternary ...sedimentary bodies, each characterised by its unique landform features. The formation of Quaternary sedimentary bodies and their features derive from the dominant building sedimentary processes. In recent years, studies of Quaternary sedimentary bodies and processes have been greatly aided by the use of digital elevation models (DEMs) derived by airborne laser scanning (ALS). High-resolution DEMs allow detailed mapping of sedimentary bodies, detection of surface changes, and recognition of the building sedimentary processes. DEMs are often displayed as hillshaded reliefs, the most common visualisation technique, which suffers from the limitation of a single illumination source. As a result, features can be barely visible or even invisible to the viewer if they are parallel to the light source or hidden in the shadow. These limitations become challenging when representing landforms and subtle landscape features in a diverse alpine topography. In this study, we focus on eleven visualisations of Quaternary sedimentary bodies and their sedimentary and morphological features on a 0.5 m resolution DEM. We qualitatively compare analytical hillshading with a set of visualisation techniques contained in the Raster Visualisation Toolbox software, primarily hillshading from multiple directions RGB, 8-bit sky view factor and 8-bit slope. The aim is to determine which visualisation technique is best suited for visual recognition of sedimentary bodies and sedimentation processes in complex alpine landscapes. Detailed visual examination of previously documented Pleistocene moraine and lacustrine deposits, Holocene alluvial fans, scree deposits, debris flow and fluvial deposits on the created visualisations revealed several small-scale morphological and sedimentary features that were previously difficult or impossible to detect on analytical hillshading and aerial photographs. Hillshading from multiple directions resulted in a visualisation that could be universally applied across the mountainous and hilly terrains. In contrast, 8-bit sky view factor and 8-bit slope visualisations created better visibility and facilitated interpretation of subtle and small-scale (less than ten metres) sedimentary and morphological features.
This paper summarizes the observation of the Potoška planina landslide, which is located in the Karavanke mountain range in NW Slovenia. The landslide lies at the tectonic contact between the Upper ...Carboniferous and the Permian clastic rocks, and the Upper Triassic to Lower Jurassic carbonate rocks. Due to active tectonics, the clastic rocks are heavily deformed and, consequently, highly prone to fast and deep weathering. The carbonate rocks are also highly fissured due to tectonic disturbances, which result in large quantities of talus and scree material covering the part below the crown. A greater spatial density of springs and wetlands, supplied from the infiltration, is evident at the contact between scree and clastic rocks. Due to prevailing geological, tectonic and hydrological conditions, the Potoška planina area is highly prone to different slope mass movements. This paper presents the monitoring of surface movement patterns at the toe of the Potoška planina landslide. The sliding mass is composed of tectonically deformed and weathered Upper Carboniferous and Permian clastic rocks covered with a large amount of talus material, which is unstable and prone to landslides. Additionally, the Bela torrent causes significant erosion and increases the possibility of mobilization of the sliding mass downstream. Based on said conditions and field survey work, the toe of the landslide is considered to be the most active part of the landslide. In order to estimate surface movement patterns over a monitoring period of 22.5 months and five reconnaissance campaigns, periodic monitoring was conducted using unmanned aerial vehicle (UAV)-based photogrammetry, which provides high-resolution images and tachymetric geodetic measurements that enable accurate control of photogrammetric analysis of surface displacements. Using the results of said periodic monitoring, analyses of UAV-based displacement patterns, surface elevations and volume changes were all modelled for four observation periods. According to our results, the movement pattern at the toe of the Potoška planina landslide indicates a steadily downslope movement of the entire area with localized surges superficial slips.
In order to transcend the challenge of accelerating the establishment of cadastres and to efficiently maintain them once established, innovative, and automated cadastral mapping techniques are ...needed. The focus of the research is on the use of high-resolution optical sensors on unmanned aerial vehicle (UAV) platforms. More specifically, this study investigates the potential of UAV-based cadastral mapping, where the ENVI feature extraction (FX) module has been used for data processing. The paper describes the workflow, which encompasses image pre-processing, automatic extraction of visible boundaries on the UAV imagery, and data post-processing. It shows that this approach should be applied when the UAV orthoimage is resampled to a larger ground sample distance (GSD). In addition, the findings show that it is important to filter the extracted boundary maps to improve the results. The results of the accuracy assessment showed that almost 80% of the extracted visible boundaries were correct. Based on the automatic extraction method, the proposed workflow has the potential to accelerate and facilitate the creation of cadastral maps, especially for developing countries. In developed countries, the extracted visible boundaries might be used for the revision of existing cadastral maps. However, in both cases, the extracted visible boundaries must be validated by landowners and other beneficiaries.
Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning ...to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment of a water bodies dataset, automatically detected from Sentinel-1 imagery, for karst flood research. Statistical and visual analyses were conducted to assess the reliability and effectiveness of the dataset. Spearman’s correlation coefficients were employed for statistical analysis to determine the degree of correlation between the areas of water bodies dataset and official water level data. Visual analyses involved the creation of heat maps based on the identified water areas, which were then compared to official flood maps, and the preparation of an analysis of historical flood events or results of hydrological and hydraulic modelling. Additionally, vegetation maps were produced to identify areas that lacked detection and complemented the heat maps. Statistical assessment showed a strong correlation (≥0.6) between the dataset and official water level data in smaller flood-prone areas with less complex inflow. Visual analyses using heat maps and vegetation maps effectively identified frequently flooded areas but had limitations in areas with dense vegetation. Comparisons with flood maps showed an important value of the dataset as an additional source of information for karst flood studies. This assessment highlights the dataset’s potential in combination with other data sources and modelling approaches.
This paper presents visualisation techniques of high-resolution digital elevation models (DEMs) for visual detection of archaeological features. The methods commonly used in archaeology are reviewed ...and improvements are suggested. One straightforward technique that has so far not been used in archaeology – the shift method – is presented. The main purpose of this article is to compare and evaluate different visualisation methods. Two conclusions have been reached. Where a single method must be chosen – for printing or producing digital images for non-professionals – the use of sky view factor or slope gradient is endorsed, both presented in greyscale. Otherwise interpreters should choose different techniques on different terrain types: shift on flat terrain, sky view factor on mixed terrain, slope gradient on sloped terrain and sky view factor (preferably as a composite image with slope gradient) on rugged terrain.
► Comparison of visualisation techniques of lidar-derived DEMs for archaeology. ► We find that there is no single best method. ► We conclude that different methods must be chosen on different terrain types. ► As a single overall method the use of sky view factor or slope gradient is endorsed.
Crop classification is an important task in remote sensing with many applications, such as estimating yields, detecting crop diseases and pests, and ensuring food security. In this study, we combined ...knowledge from remote sensing, machine learning, and agriculture to investigate the application of transfer learning with a transformer model for variable length satellite image time series (SITS). The objective was to produce a map of agricultural land, reduce required interventions, and limit in-field visits. Specifically, we aimed to provide reliable agricultural land class predictions in a timely manner and quantify the necessary amount of reference parcels to achieve these outcomes. Our dataset consisted of Sentinel-2 satellite imagery and reference crop labels for Slovenia spanning over years 2019, 2020, and 2021. We evaluated adaptability through fine-tuning in a real-world scenario of early crop classification with limited up-to-date reference data. The base model trained on a different year achieved an average F1 score of 82.5% for the target year without having a reference from the target year. To increase accuracy with a new model trained from scratch, an average of 48,000 samples are required in the target year. Using transfer learning, the pre-trained models can be efficiently adapted to an unknown year, requiring less than 0.3% (1500) samples from the dataset. Building on this, we show that transfer learning can outperform the baseline in the context of early classification with only 9% of the data after 210 days in the year.
Detailed spatial data on grassland use intensity is needed in several European policy areas for various applications, e.g., agricultural management, supporting nature conservation programs, improving ...biodiversity strategies, etc. Multisensory remote sensing is an efficient tool to collect information on grassland parameters. However, there is still a lack of studies on how to process, combine, and implement large radar and optical image datasets in a joint observation framework to map grassland types on large heterogeneous study areas. In our study, we assessed the usefulness of 2521 Sentinel-1 and 586 Sentinel-2 satellite images and topographic data for mapping grassland use intensity. We focused on the distinction between intensively and extensively managed permanent grassland in a large heterogeneous study area in Slovenia. We provided dense Satellite Image Time Series (SITS) for 2017, 2018 and 2019 to identify important differences, e.g., management practices, between the two grassland types analysed. We also investigated the effectiveness of combining two different remote-sensing products, the optical Normalised Difference Vegetation Index (NDVI) and radar coherence. Grassland types were distinguished using an object-based approach and the Random Forest classification. With the use of SITS only, the models achieved poor performance in the case of cloudy years (2018). However, the performance improved with additional features (environmental variables). The feature selection method based on Mean Decrease Accuracy (MDA) provided a deeper insight into the high-dimensional multisensory SITS. It helped select the most relevant features (acquisition dates, environmental variables) that distinguish between intensive and extensive grassland types. The addition of environmental variables improved the overall classification accuracy by 7–15%, while the feature selection additionally improved the final overall classification accuracy (using all available features) by 2–3%. Although the reference dataset was limited (1259 training samples), the final overall classification accuracy was above 88% in all years analysed. The results show that the proposed Random Forest classification using combined multisensor data and environmental variables can provide better and more stable information on grasslands than single optical or radar data SITS on large heterogeneous areas. Therefore, a combined approach is recommended to distinguish different grassland types.
This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image ...to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.
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