Following the launch of the Copernicus Sentinels, which has enabled the free access to petabytes of satellite data, change point analysis has caught the attention of the remote sensing community. In fact, the exploitation of satellite image time series has a number of advantages over the use of an image pair, as it allows a better understanding of how the process under study is evolving. Although this is a well-known area of research that spans different application domains, the majority of the change point detection methods have been designed for the analysis of univariate signal, and only a few of them can be used to process multidimensional data. In this paper, we present a novel change point detection method based on the combination of wavelets and mathematical morphology for the analysis of data cubes of satellite image time series that allows the user to reduce the data dimensionality at the input level. We conducted a preliminary performance assessment on 50 sites in Belgium using up to 5 different input features derived from Sentinel-1 and Sentinel-2 data.

The all-weather capabilities of SAR satellite sensors make them a powerful tool for the regular and consistent monitoring of large areas of interest. In particular, they could be used to gather information that would be otherwise difficult to obtain with in-situ campaigns, as for instance when it comes to assess the extent of damages in the aftermath of disastrous events. In this paper we show how the PELT change point detection algorithm can be used to analyze time series of Sentinel-1 images for rapid damage mapping in areas of conflict. Despite the sensor’s limited spatial resolution, the results show that, even with relatively short time series and using both polarizations, it is possible to achieve satisfactory detection rates.

The Walloon Region is currently managing a database of more than 2000 ‘redevelopment sites', i.e. urban sites that were previously used for industrial activities and/or housing and that are now abandoned. The administration needs to keep this inventory up-to-date so that the necessary urban planning could be done; however, at the moment, this information is obtained via time-consuming on field campaigns. Thanks to the launch of the Copernicus programme, free satellite data are now provided at high temporal resolution, and new monitoring approaches can be implemented. Leveraging a well-established changepoint detection method, this paper shows some preliminary results on how time series of Sentinel-1 and Sentinel-2 data could be jointly used to automatically detect changes in urban areas, thus providing the Walloon Region with a tool that can be exploited for a more efficient management of the ‘redevelopment sites'.

The “Compagnie Sucrière Sénégalaise” (CSS) wanted to upscale the field-level crop simulation model AquaCrop (FAO's agro-meteorological model) for the automated monitoring and management of its ±13.000 ha of irrigated sugarcane. A recently developed changepoint detector was applied to Sentinel-1 time series to identify key phenological crop stages for assimilation in AquaCrop. Field-specific emergence dates, varying from 10 to 45 days after planting, were assimilated in AquaCrop. Simulated sugarcane biomass had an R2 of 0.7 and an RMSE of 6.4%. The improved management support system was also able to identify potential irrigation mismanagements.

An accurate model of yield prediction will benefit many aspects of managing growth and harvest of sugarcane crops. In this study Sentinel-1 and Sentinel-2 time-series were used to automatically detect harvest dates of sugarcane fields in the Far North Queensland of Australia. Harvest date information was further used in combination with weather, soil and elevation data to predict sugarcane yield at different time steps over three consecutive growing seasons using machine learning. Our results suggest that harvest dates could be identified with detection rates of 87% and 91% using Sentinel-1 and Sentinel-2 imagery, respectively. Similarly, sugarcane yield could be predicted using Sentinel-1 and Sentinel-2 satellite imagery in conjunction with other geographical attributes with accuracy of 65% as early as 180 days after the previous harvest.

Sugarcane is the world's largest crop by production quantity, as reported by the Food and Agriculture Organization of the United Nations. Its growth cycle has a duration of about 12-14 months, and the same plantation can be generally harvested up to 7 times before replanting is needed. In order to both predict the yield and optimize the production processes, sugarcane industries need to be regularly updated on the harvest progress; however, they mainly rely on direct communications from farmers, and this has evident limitations.In this paper we present a method that exploits stacks of Sentinel-1 images for the automatic detection of sugarcane harvest dates. The method has been used to monitor, over a period of 21 months, a large cultivated area in Northern Senegal that comprises 719 sugarcane parcels.The results have shown that the method performs well in terms of both detection and estimation accuracy, and has the potential to be operationally used in the sugarcane production processes.

This paper presents the benchmarking of four ship detection systems based on satellite borne Synthetic Aperture Radar data. This research, carried out within the framework of the FP7 NEREIDS project, provides a detailed performance assessment of the four detectors, not only in terms of detection accuracy, but also showing how they can cope with challenging situations typical of the maritime environment. Despite the good results, the conclusions are that none of the detection systems behave well in all of the conditions. Nevertheless, merging all the different ship detection reports would increase the performances.

This paper presents an electromagnetic (EM) technique for the reconstruction of the physical and geometrical properties (permittivity and thickness) of stratified media. The key points of the approach, belonging to the so-called layer stripping algorithms, are the introduction of an equalization step that takes into account propagation effects, and the design of a procedure devoted to multiple reflections' removal. Furthermore, the proposed main processing block is an energy-based method able to accurately estimate amplitudes and time of delays of backscattered echoes in the time domain. A numerical analysis of the algorithm's potentialities will show that it can be successfully employed under different working conditions and in the presence of noisy data.

In this paper, an inversion ANN-based algorithm for the estimation of geophysical properties (i.e. thickness and permittivity) of subsurface layers in stratified geometries is presented. The basic procedure for the analysis of GPR scans of single subsurface layers placed over a uniform background recently proposed by the authors has been here extended and inserted into a general framework where each stratum is recursively processed.

In this paper, a preliminary study, based on GPR data analysis by means of artificial neural networks, for automatic pavement diagnostic is addressed. The proposed solving solution models the pavement as a multi-layered medium composed of N parallel homogenous layers, which are separately analyzed through a recursive procedure able to reconstruct their permittivity and thickness. The basic processing module of the whole procedure, is here presented.

This paper presents two methods to fuse SAR and optical data for urban extent extraction. The two methodologies build over single sensor's procedure in order to improve the efficiency of the characterization of the urban environment when more data is available. Results over Pavia and Al Fashir conform the effectiveness of the proposed procedures.

In this paper a novel method, based on autocorrelation indexes and gray-level co-occurence matrix, for the extraction of urban areas in high resolution SAR images, is presented. It strongly reduces human interpreters' intervention thanks to a high degree of automation within the processing chain and allows a fast and accurate generation of built-up area maps, which can be employed for land mapping and support in relief operations.