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This paper presents a novel approach named extinction profiles to model the spatial information of remote sensing images. Then, the output of the extinction profile is fed to a grid-search random forest classification method. Results indicate that the proposed approach can effectively extract spatial information from remote sensing gray scale images and provide high classification accuracies in an...
New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with...
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and...
The SPOT 6–7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. In order to significantly improve the SPOT cloud detection and get rid of frequent manual re-labelings, we study a new automatic cloud detection technique that is adapted to large datasets. The proposed method is...
A probability graph model can effectively model spectral and spatial dependencies within remote sensing images for land cover classification. The most common structure used to unify this probabilistic information is a second order Markov network that encapsulate unary and pairwise potentials. In this paper we explore various heuristics to discover new graph structures that will assist with classifying...
The current paper aims to provide a brief overview of the ESA activities relevant to Polarimetric SAR missions mission development and exploitation, present achievements and discuss future opportunities for research.
Many applied Earth observation problems are based on land cover and land use maps, derived from satellite data. That is why it is important to assess their accuracy. We have developed retrospective regional 30 meter resolution land cover maps for Ukraine based on Landsat data for 1990, 2000 and 2010. As there is no reference data for validating retrospective periods, validation of the maps could be...
The exploitation of the high revisit time (8–16 days) by the COSMO-SkyMed® (CSK®) satellites is an important opportunity for agricultural mapping. This study aims at evaluating CSK® potentiality to classify different crop types, with CSK® multi-temporal images collected over the agricultural site of Marchfeld, in Austria. Two different time series of CSK® HIMAGE SAR scenes, at 3m resolution, 9 at...
This paper evaluates two soft/fuzzy supervised classification algorithms for coral reef mapping using Landsat-8 satellite images. The work is unique in its nature, since it introduces for the first time supervised soft pixel classifiers for coral reef mapping. A comparison was made between the fuzzy maximum likelihood (FML) and a supervised version of the fuzzy c-means (FCM) with traditional hard...
Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers,...
Information on the spatio-temporal dynamics of inland water bodies is of high value for many applications, for example in the context of water and land management or for ecosystem service assessments. In this study, different approaches to delineate inland water bodies from MODIS 250 m time series were compared. Here, the performance of different input bands and indices, of trainings pixel selection...
Most crop classification work use the ground reference data to training the classifier; but sometimes, the ground reference data cannot be obtained. In this paper, we tried to use the NDVI time series obtained during 2006 and 2013 to classify crop types in 2014 at 30 m spatial resolution. The experiment was conducted in Southeast Kansas, USA. Firstly, we extracted the NDVI time series using ground...
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