Bathymetry from satellite images: a proposal for adapting the band ratio approach to IKONOS data

The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images

METHODS FOR SATELLITE DERIVED BATHYMETRY FROM SENTINEL-2 IMAGES: A COMPARISON

In recent decades, research has been developed to estimate near-shore bathymetry depth values using satellite imagery. Visible and infrared bands are used to derive elevation profile estimates, so to obtain bathymetric in rapid way without mobilisation of persons or equipment and saving the costs. For consequence, Satellite Derived Bathymetry (SDB) is seen as a valid approach for shallow waters survey: strongly supported by the activity of scholars and researchers, multiple methods are available in the literature. This article aims to investigate and compare different SDB methods for sea depth extraction from Sentinel-2 satellite multispectral images, with particular attention to the accuracy of the results. The experiments are conducted on imagery including Blue, Green, Red and Near Infrared bands, with 10 m resolution, concerning the Bay of Pozzuoli (Italy). After removing the glint, the effects caused by the reflection of sunlight through single scattering from sea surface, three methods are applied: Band Ratio method (BRM), 3rd-degree polynomial regression line method (3DPM), and principal component analysis method (PCAM). 3DPM can be seen as a variant of the BRM where the linear law that interprets the correlation between the band ratio values and the depth values is replaced by the third order function. Models are trained using depth data extracted from an Electronic Navigational Chart (ENC) at 1:7,500 scale, which is also used to verify result accuracy. The experiments demonstrate that the 3DPM is better able to obtain a more precise bathymetric model, confirming the greater adaptability of the 3rd order function to interpretate the variability of the interaction of light with water along the water column

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance

Sarcopenia e fratture vertebrali da fragilità : studio retrospettivo

Sarcopenia e fratture vertebrali da fragilità : studio retrospettiv

The Influence of Interpolation Methods and point density on the Accuracy of a Bathymetric Model

The construction of three-dimensional models of the seabed starting from sample data requires the use of interpolators to calculate the depth where it has not been measured. The accuracy of the model depends on several factors, e.g., the interpolation method, the seabed morphology, the density and distribution of the samples. This article aims to investigate the accuracy of bathymetric models in relation to the interpolation methods and the number of points available. Eight different methods available in ArcGIS software are analyzed in this study, including 6 deterministic methods, i.e., Inverse distance weighting (IDW), and 5 variations of Radial Basis Functions (RBFs). Additionally, two stochastic methods, such as Universal Kriging (UK) and Ordinary Kriging (OK), are also examined. The experiments are carried out using the bathymetric information from an Electronic Navigational Chart (ENC) at a scale 1:30,000 concerning the north-eastern sector of the Gulf of Naples. The 12,638 depth points including in the ENC are organized in four datasets presenting different data density (25%, 50%, 75% and 100% of the available data respectively). The results of the study confirm that the accuracy of the models improves as the number of points used increases. Specifically, RBF interpolators are found to be more effective than other methods at low density values (25% and 50% of available data) while Kriging interpolators outperform other methods when using large numbers of points (75% and 100% of available data)

Accuracy evaluation of coastline extraction methods in remote sensing: a smart procedure for Sentinel-2 images

Different algorithms are available in literature to extract coastline from remotely sensed images and different approaches can be adopted to evaluate the result accuracy. In every case, a reference coastline is suitable to compare alternative solutions: usually, the visual photointerpretation on the RGB composition of the considered imagery and the manually vectorization of the coastline allow an accurate term of comparison, but they are laborious and time consuming. This article aims to demonstrate that a smart procedure is possible using a LiDAR-generated Digital Elevation Model (Lg-DEM) as a useful source from which to rapidly extract the reference coastline. The experiments are carried out on Sentinel-2 imagery, using six indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Enhanced Vegetation Index (EVI), Red-Green Ratio (RGR) and NIR-Red Ratio (NRR). The unsupervised classification algorithm named K-Means transforms each index resulting product in two clusters, i.e. water and no-water, while the automatic vectorization allows to detect the coastline as separation between land and sea. The coastline from Lg-DEM and the manually achieved one using photointerpretation are both assumed as references for testing result accuracy. In every case, the performance analysis of the six indices products induces similar results, confirming the combination of NDWI and K-Means as the most performing approach. The tests demonstrate that, when Lg-DEM and satellite images concern the same area in the same period or in absence of variations, the coastline extracted from Lg-DEM is useful as reference to compare various methods

Machine Learning Approaches for Coastline Extraction from Sentinel-2 Images: K-Means and K-Nearest Neighbour Algorithms in Comparison

In the last decades, machine learning techniques have increasingly spread to many application fields of remote sensing and, more recently, have also involved the extraction of the coastline from satellite images. The presence of different machine learning algorithms as well as the availability of different types of remote sensing data, make it necessary to further investigate in order to identify methodological solutions for providing accurate results. This article aims to compare two alternative and typical methodological approaches of machine learning, one unsupervised, the other supervised, represented respectively by the K-Means (KM) and K-Nearest Neighbour (KNN) algorithms. The experiments are conducted on Sentinel-2 satellite images, limited to the bands with the highest geometric resolution (10 m). The dataset includes also the image resulting from the application of the Normalized DifferentWater Index (NDWI), which is particularly effective for distinguishing water/non-water. The coastline obtained by manual vectorization on the Sentinel-2 RGB composition is the term of comparison for evaluating the result accuracy. The DistributedRatio Index (DRI) is applied for this purpose. The use of training sites with the KNN method allows to obtain a more reliable classification in the presence of multiple spectral bands. On the contrary, using only the NDWI layer theKMmethod produces better results, demonstrating how in this case the land-sea distinction is clearer and the automatic clustering, as it is not affected by human error that accompanies the detection of the training sites, is more reliable

Using electronic navigational chart for 3D bathymetric model of the Port of Naples

Nautical charts generally report fundamental knowledge for the safety of navigation. This information also includes sea depth data reported as depth points or contour lines, which can be used to build a 3D model of the seabed. However, there are different interpolation methods for creating digital depth models, and there is no way to know in advance which of them is the best performing. The aim of this work is to compare different spatial interpolation methods applied on a dataset concerning the seabed of the Port of Naples (Italy) and extracted from the Electronic Navigational Chart (ENC) produced by the Hydrographic Institute of the Italian Navy, in scale 1:10.000. Four deterministic interpolation methods, i.e. Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Local Polynomial Interpolation (LPI), Radial Basis Function (RBF), and two stochastic interpolation methods, i.e. Ordinary Kriging (OK) and Universal Kriging (UK), are applied using Geographic Information System (GIS) software. Since each method requires to set specific parameters and different options are available, e.g. the order of the polynomial function for GPI and LPI, or semi-variograms for OK and UK, twenty-three models are generated. The result quality is evaluated by Leave-One-Out cross-validation and the statistics of the residuals produced by each interpolation method in the measured points are compared and analysed. The experiments confirm that the stochastic approach is more versatile compared to deterministic approach and can produce better results, as it is testified by the great performance of the Ordinary Kriging, which produces the most accurate 3D models

Induction chemotherapy in head and neck cancer patients followed by concomitant docetaxel based radiochemotherapy

Concurrent chemoradiotherapy has become the standard of care for patients with inoperable squamous cell head and neck carcinoma. More recently, induction chemotherapy has been adopted as an approach in the management of these patients. We report the results of a phase II trial associating induction chemotherapy and concomitant chemoradiotherapy in a series of patients with inoperable squamous cell head and neck cancer. Twenty-nine patients with advanced squamous cell carcinoma ineligible for surgery were enrolled. Induction chemotherapy with docetaxel 75 mg/m2 and cisplatin 75 mg/m2 every 21 days was administered for two cycles. Radiotherapy followed the induction phase. During radiotherapy, docetaxel was administered weekly at the dose of 33 mg/m2. Primary end point of the study was feasibility of treatment. Six (18%) patients failed to conclude the treatment schedule. Although response rates in evaluable patients were very high (disease control rate >90%), toxicities were a matter of concern. The reported treatment schedule proved infeasible. However, some modifications in ancillary therapies aimed at exploiting its efficacy could make it practicable