Guido Lemoine

Bio: Guido Lemoine is an academic researcher from Institute for the Protection and Security of the Citizen. The author has contributed to research in topics: Synthetic aperture radar & Radar imaging. The author has an hindex of 19, co-authored 47 publications receiving 2467 citations.

Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery

Abstract: Rapid damage assessment after natural disasters (eg, earthquakes) and violent conflicts (eg, war-related destruction) is crucial for initiating effective emergency response actions Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner In this paper, we present a novel method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated First, the 3-D parameters of a building are estimated from the pre-event optical imagery Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event Third, the similarity between the predicted image and the actual SAR image is analyzed If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed A similarity threshold is used to classify the buildings We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily damaged in the Sichuan earthquake of May 12, 2008 For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data

A study of vegetation cover effects on ERS scatterometer data

Abstract: The scatterometer flown onboard the European remote-sensing satellites ERS-1 and ERS-2 is a vertically polarized radar operating at 5.3 GHz (C-band) and has a spatial resolution of 50 km. In a number of studies, the sensitivity of the ERS scatterometer to vegetation has been demonstrated, but it is not yet clear which vegetation parameters are of primary importance to explain the ERS scatterometer signal. In this paper, the effects of land cover and seasonal vegetation development are investigated by comparing ERS scatterometer data with land cover information, normalized difference vegetation index (NDVI) data sets, and meteorological observations. As a study area, the Iberian Peninsula was chosen. The Iberian Peninsula is characterized by the Mediterranean climate that has a wet winter and a dry summer. This allows the authors to better differentiate the effects of the annual vegetation and precipitation cycle on the temporal evolution of the backscattering coefficient /spl sigma//spl deg/. It is shown that the ERS scatterometer has only limited capabilities for monitoring the vegetation development within a given year because most of the temporal variability of /spl sigma//spl deg/ is due to soil moisture changes. On the other hand, it might be of merit for vegetation discrimination on large scales (regional to global) because the percentage area of forests, bushes, and shrubs within one ERS scatterometer pixel is found to explain a significant part of the spatial variability of the signal.

Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data

Abstract: For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. Satellite imagery is extremely valuable source of data to provide crop maps in a timely way at moderate and high spatial resolution. Information on parcel boundaries that takes into account the spatial context may improve the quality of maps compared to pixel-based classification approaches. In general, parcels may contain several plots with different crops and such situations should be taken into account when using parcel boundaries. In this paper, we aim to compare pixel-based and parcel-based approaches to crop classification from multitemporal optical (Landsat-8) and synthetic-aperture radar (SAR) Sentinel-1 imagery. For this, we propose a parcel-based approach that involves a pixel-based classification map and specifically designed rules to account for several plots within parcel. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring test site in Ukraine covering the Kyiv oblast (North of Ukraine) in 2013–2015, and the Odessa oblast (South of Ukraine) in 2014–2015. We found that pixel-based overall classification accuracy can be increased from 85.32% to 89.40% when using parcel boundaries. Among tested parcel-based approaches, the one that relied on pixel-based classification map and a procedure to select multiple plots within the parcel yielded the best performance.

Building Height Retrieval From VHR SAR Imagery Based on an Iterative Simulation and Matching Technique

Abstract: Experimental airborne synthetic aperture radar (SAR) systems achieve spatial resolutions of approximately 10 cm, whereas the new spaceborne very high spatial resolution (VHR) SAR sensors onboard the TerraSAR-X and COSMO-SkyMed satellites achieve spatial resolutions down to 1 m. In VHR SAR data, features from individual urban structures (i.e., buildings) can be identified by their characteristic settings in urban settlement patterns. In this paper, we present a novel concept for the height estimation of generic man-made structures from single detected SAR data. The proposed approach is based on the definition of a hypothesis on the height of the building and on the simulation of a SAR image for testing that hypothesis. A matching procedure is applied between the estimated and the actual SAR image in order to test the height hypothesis. The process is iterated for different height assumptions until the matching function is optimized, and thus, the building height is estimated. The efficiency of the proposed method is demonstrated on a set of 40 flat- and gable-roof buildings using two submeter VHR airborne and two 1-m resolution TerraSAR-X SAR scenes all acquired from the same residential area in Dorsten, Germany. The results show that, in the absence of string disturbing effects, the method is able to estimate the height of flat- and gable-roof buildings in the submeter data to the order of a meter, while the accuracy for the meter resolution spaceborne data is lower but still sufficient to estimate the number of floors of a building.

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Abstract: Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).

The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Abstract: . In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu ) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.