The aim of this paper is to evaluate the level of flooding in proximity of sensible targets in urban areas using only one Synthetic Aperture Radar (SAR) image. To this purpose a two-step algorithm is here proposed: first the flooded areas are detected in the SAR image; and then the water level is retrieved by inverting scattering models developed for urban areas and now properly adapted for the case at issue. The retrieval is performed through a local approach where the a-priori knowledge of the target ground truth and two gauges in the premises is required. The approach is tested on a High Resolution (HR) TerraSAR-X image acquired during the flooding occurred in the Gloucestershire in July 2007. © 2011 IEEE.
Guida R, Ng SW, Iervolino P (2015) S- and X-band SAR Data Fusion,2015 IEEE 5TH ASIA-PACIFIC CONFERENCE ON SYNTHETIC APERTURE RADAR (APSAR) pp. 578-581
Guida R, Iervolino P, Freemantle T, Spittle S, Minchella A, Marti P, Napiorkowska M, Howard G, Arana HH, Alvarado SC (2016) Earth Observation for the Preservation of the Bacalar Area,Living Planet Symposium 2016
Iervolino P, Guida R, Whittaker P (2015) A NEW GLRT-BASED SHIP DETECTION TECHNIQUE IN SAR IMAGES, 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) pp. 3131-3134 IEEE
One of the main goals of the State is to guarantee the security and welfare of the citizens. States have agreed in making ?a better world? for citizens under the United Nations (UN) Sustainable Development Goals (SDG) targets and actions. States have acquired the obligation to address this mandate and seek all possible solutions to address it. International cooperation and the use of space technology are tools to achieve this endeavor. This paper discusses the innovations of international cooperation introduced by States and its impact in law-making agreements focusing on Climate Change effects and the protection of the environment under the SDG 13. It explains the innovation in cooperation and law-making procedures by taking as an example the UK-Mexico cooperation for the protection of the Bacalar region and its ecosystem within the International Partnership Space Programme (IPSP). Within this scenario, an Earth observation (EO) product for the flood detection using Synthetic Aperture Radar (SAR) images is presented and efficiently tested over two images acquired during the Hurricane Dean in 2007.
Disclaimer: The information and contents in this paper are solely the original work of the author and does not claim to represent the views of the UKSA, AEM and/or its space partners.
Ship detection with synthetic aperture radar (SAR)
images, acquired at different working frequencies, is presented
in this paper where a novel technique is proposed based on the
generalized-likelihood ratio test (GLRT). Suitable electromagnetic
models for both the sea clutter and the signal backscattered from
the ship are considered in the new technique in order to improve the
detector performance. The GLRT is compared to the traditional
constant false alarm rate (CFAR) algorithm throughMonte?Carlo
simulations in terms of receiver operating characteristic (ROC)
curves and computational load at different bands (S-, C-, and X-).
Performances are also compared through simulations with different
orbital and scene parameters at fixed values of band and
polarization. The GLRT is then applied to real datasets acquired
from different sensors (TerraSAR-X, Sentinel-1, and Airbus airborne
demonstrator) operating at different bands (S-, C-, and X-).
An analysis of the target-to-clutter ratio (TCR) is then performed
and detection outcomes are comparedwith an automatic identification
system data when available. Simulations show that the GLRT
presents better ROCs than those obtained through the CFAR algorithm.
On the other side, results on real SAR images demonstrate
that the proposed approach greatly improves the TCR (between
22 and 32 dB on average), but its computational time is 1.5 times
slower when compared to the CFAR algorithm.
Extensive work has been carried out on detecting ships using
space-borne Synthetic Aperture Radar (SAR) systems. However,
the identification of small vessels is still challenging especially
when the sea conditions are rough. In this work, a
new detector is proposed based on dual-polarized incoherent
SAR images. Small ships have a stronger cross polarization
accompanied by a higher cross- over co-polarization
ratio compared to sea. This is the rational at the base of the
The new detector is tested with dual-polarization HH/HV
PINGPONG Cosmo-SkyMed images acquired over the North
Sea. The test area is near Rotterdam where a large number of
ships are expected.
This paper presents a ship-detection study with Synthetic Aperture Radar (SAR) images acquired at two different frequencies: X- and C-band. The detection procedure relies on a novel algorithm based on the likelihood functions of both canonical ship target and sea clutter. Spaceborne images were acquired over the same area in the Solent Channel in UK at approximately the same time on the 7th June 2016. Here, datasets are compared in terms of probability of detection (PD), probability of false alarm (PFA) and Target-to-Clutter Ratio (TCR). Detection maps are validated with Automatic Identification System (AIS) data when available and preliminary results show a higher TCR for the X-band SAR image.
A novel methodology is proposed for soil moisture content (SMC) estimation using the feature level fusion of Senti-nel-1 and Landsat-8 satellite datasets. This fusion consists of concatenating Temperature Vegetation Dryness Index (TVDI) to the feature vector (radar and physical features) of the inversion of the Integral Equation Model (IEM) through Artificial Neural Networks (ANN) to reduce vegetation effects on Sentinel-1 estimation. This methodology is applied on Blackwell farms, Guildford, United Kingdom, where ground truth and satellite data were collected dur-ing 2017. The preliminary SMC estimation results show lower RMSE errors (by 0.474%) and less bias than the IEM inversion method.
This paper provides a novel approach for the fusion of Synthetic Aperture Radar (SAR) images and Automatic Identification System (AIS) data for the tracking of vessels over sea areas. At this aim, SAR and AIS data are simulated and optimized for the upcoming NovaSAR-S maritime and stripmap modes. These simulated data are used to test the proposed tracking methodology in real time scenario. The results also give practical guidelines on how to task NovaSAR-S to cover uncooperative vessels over the revisit time of the satellite considering the Doppler shift due to the radial velocity of the target.
A new algorithm for land classification is presented in this paper and is based on the fusion of Multispectral, Panchromatic and Synthetic Aperture Radar (SAR) images. The novel approach relies on Generalized Intensity-Hue-Saturation (G-HIS) transform and the Wavelet Transform (WT). The fused image is derived by modulating the SAR texture with the high features details of the Panchromatic WT and by injecting this product at the place of G-HIS high feature details. Finally, a classification is performed on the fused product by using a Maximum Likelihood (ML) classifier. The algorithm has been tested on data acquired by Sentinel-1 (SAR) and Landsat-8 (Multispectral and Panchromatic) over the area of Greater Doha in Qatar in 2017. Results show an increment of 3% in the overall accuracy for the fused product compared to the Multispectral dataset.
Achiri Lotfi, Guida Raffaella, Iervolino Pasquale (2018) SAR and AIS Fusion for Maritime Surveillance,Proceedings of the 4th International Forum on Research and Technologies for Society and Industry
Institute of Electrical and Electronics Engineers (IEEE)
This paper presents a novel approach to fuse
Synthetic Aperture Radar (SAR) images and Automatic
Identification System (AIS) data for maritime surveillance. The
procedure consists of four steps. First, ship detection is performed
in the SAR image using a Constant False Alarm Rate (CFAR)
algorithm; then feature extraction (ship position, heading and size)
is performed on ships detected in the SAR image, the third step
consists in identifying the detected ships and extracting the same
features from the AIS data. The final step is to feed the fusion
block with both features vectors extracted separately from the
SAR and AIS. Here the arithmetic mean function is established.
The algorithm is tested using simulated SAR images and AIS data.
Preliminary results of the fusion of SAR and AIS data are
presented and discussed.
The request for maritime security and safety applications has increased in the recent past. In this scenario, Synthetic Aperture Radar (SAR) sensors are one of the most effective means thanks to their capability to get images independently from daylight and weather conditions.
In the SAR ship-detection field, many algorithms have been presented in literature; however none of them has ever considered the aspects behind the interaction of the electromagnetic wave between the target and the surrounding sea.
This thesis explores the electromagnetic interaction arising between the ship and the sea and, firstly, a novel model to evaluate the Radar Cross Section (RCS) backscattered from a canonical ship is derived. RCS is modelled according to Kirchhoff Approximation (KA) within the Geometric Optics (GO) solution. The probability density function relative to the double reflection contribution is derived for all polarizations and the new model is validated on SAR images showing a good match between the theoretical values and those ones measured on real SAR images.
Then, a novel ship detector, based on the Generalized Likelihood Ratio Test (GLRT) where both the sea and the ship electromagnetic models are considered, is proposed. The GLRT is compared to the CFAR algorithm through Monte Carlo simulations in terms of ROCs (Receiver Operating Characteristic curves) and computational load at different bands (S, C and X). Performances are also compared through simulations with different orbital and scene parameters. The GLRT is then applied to datasets acquired from different sensors operating at different bands: the Target to Clutter Ratio (TCR) is computed and detection outcomes are compared with AIS data. Results show that the GLRT presents better ROCs and greatly improves the TCR, but its computational time is slower when compared to the CFAR algorithm.
Finally, a new approach for ship-detection and ambiguities removal in LPRF (Low Pulse Repetition Frequency) SAR imagery is proposed. The method exploits the range migration pattern and is evaluated on a downsampled SAR image. The algorithm is able to reject the SAR azimuth ambiguities and can be adapted for the upcoming Maritime Mode of the future NovaSAR-S sensor.
This paper aims at developing a new enhanced algorithm for mapping semi-arid areas based on fusion techniques of Synthetic Aperture Radar (SAR) and Light Detection And Ranging (LIDAR) datasets. Firstly, both datasets are preprocessed to remove geometric and radiometric errors; then features of interest are extracted from SAR and LiDAR products to build masks and identify meaningful classes. Finally, classification results are refined with morphological filters. The new algorithm has been tested on data acquired by TerraSAR-X and an airborne LiDAR sensor over the Natural Reserve of Maspalomas in Canary Islands. Results show an overall classification accuracy of 85% with an absolute increment of more than 14% compared to a classification in which only LiDAR data are used.
The Urban Heat Island (UHI) effect is defined as an increase of the air and surface temperature inside a city compared to surrounding rural areas. This increment can be of several degrees, thus exposing populations to serious health risks, especially in hot developing countries, where the majority of the world?s megacities is located. The UHI effect has been widely studied in the past with local methods employing field sensors. The use of satellites moved the analysis from local to city scale, but long-term investigations have been so far limited by storage and computational capacities. In this work, both ESA and NASA heritage data are used to study the temporal evolution (2003-2017) of the UHI of the city of Chennai, India. The Google Earth Engine is exploited to process the available large dataset in a reasonable time. Results show that the UHI of Chennai has grown over time and that its main drivers are the average temperature and the city expansion.
Land cover mapping is one of the classic applications of synthetic aperture radar remote sensing. However, despite of the algorithmic progress in classification techniques, the semantic content of available maps does remain unchanged, with only a few macro-classes (like water, forest, urban, and bare soil) being discriminated in the majority of the works from past years. In this paper, a methodology to extract a higher level semantics from synthetic aperture radar images is presented. It is based on coupling pixel-based clustering with object-based image analysis and contextual information. Preliminary results have been produced from multitemporal SAR datasets over a forest area in Colombia. They demonstrate that the synergic exploitation of pixel and object information can provide higher quality land cover results and more information to map users.
In the Synthetic Aperture Radar (SAR) framework many detection algorithms and techniques have been published in the recent literature; however the detection of vessels whose dimensions are in the order of the image spatial resolution is still challenging in rough sea state scenarios. This issue is addressed in the paper presented here by comparing rationale and performance of two detectors developed by the same authors: the Generalized Likelihood Ratio Test (GLRT) and the Intensity Dual-Polarization Ratio Anomaly Detector (iDPolRAD). Both detectors are tested on a dual-polarization VV/VH Interferometric Wide Swath Sentinel-1 image acquired over the Suruga Bay on the Pacific Coast of Japan. The theory is presented here and the two detectors are compared against the Cell Average-Constant False Alarm Algorithm (CA-CFAR) showing both better performance than CFAR in terms of false alarms rejection.
A novel decision level data fusion algorithm for soil
moisture content estimation is proposed in this paper.
Firstly, individual estimations are determined, respectively,
from the inversion of the Integral Equation Model (IEM) for
Sentinel-1 and from the Temperature Vegetation Dryness
Index (TVDI) for LANDSAT-8. Then, a feature level fusion
of these methods is performed using an Artificial Neural
Network (ANN). Finally, all estimations including the
feature level fusion estimation are fused at the decision level
using a novel weights based estimation. The area of interest
for this study is Blackwell Farms, Guildford, United
Kingdom and datasets were taken on 17/11/2017 for both
Landsat-8 and Sentinel-1. Estimation from the proposed
decision level fusion method produces a Root Mean Square
Error RMSE (1.090%) which is lower than RMSE of the
individual estimations of each sensor as well as that of the
feature level fusion estimation.