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Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the

Date 12-14 July 2011

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  • [Title page]

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  • Table of contents

    Page(s): v - xii
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  • A novel approach to targeted change detection

    Page(s): 1 - 4
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (835 KB) |  | HTML iconHTML  

    In several applications the objective of change detection is actually limited to identify one (or few) specific “targeted” land-cover transition(s) affecting a certain area in a given time period. In such cases, ground-truth information is generally available for the only land-cover classes of interest at the two dates, which limits (or hinders) the possibility of successfully employing standard supervised approaches. Moreover, even unsupervised approaches cannot be effectively used, as they allow detecting all the areas experiencing any type of change, but not discriminating where specific transitions of interest occur. In this paper, we present a novel technique capable of addressing this challenging issue by using the only ground truth available for the targeted land-cover classes at the two dates. In particular, it jointly exploits the expectation-maximization algorithm and an iterative labeling strategy based on Markov random fields accounting for spatio-temporal correlation. Experimental results confirmed the effectiveness and the reliability of the proposed method. View full abstract»

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  • Detection of small changes in airborne hyperspectral imagery: Experimental results over urban areas

    Page(s): 5 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1216 KB) |  | HTML iconHTML  

    In this work we investigate the problem of detecting small changes in images acquired by airborne sensors, using direct georeferencing from gyro data and GPS position. We intend to avoid the time consuming step of image registration, exploiting direct georeferencing cascaded with a robust change detection strategy that can properly manage the typical registration errors given by onboard instrumentation. We investigate the effectiveness of this approach in the urban scenarios, where we are interested in detecting changes induced by small objects. The experimental analysis conducted on real hyperspectral data with very high spatial resolution highlights the effectiveness of the proposed approach, resulting in a consistent improvement of both the capability of detecting changes and of suppressing the background. View full abstract»

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  • A multilevel approach to change detection for port surveillance with very high resolution SAR images

    Page(s): 9 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (761 KB) |  | HTML iconHTML  

    This paper proposes an approach to change detection in very high geometrical resolution (VHR) multitemporal SAR images for hot spot surveillance. The proposed approach is based on two concepts: i) the use of backscattering information extracted at different resolution levels; and ii) the use of prior information usually available on hot spots. Here the proposed approach is designed for the solution of a surveillance problem in port areas. To this end a data set was used made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (CSK®) constellation in spotlight mode over the commercial port of Livorno (Italy). These images define a complex change-detection problem due to the different kinds of changes on the ground, the high spatial resolution and the complexity of object backscattering in the considered area. Experimental results point out the effectiveness of the proposed approach. View full abstract»

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  • Change detection in very high resolution imagery based on dynamic time warping: An implementation for Haiti earthquake damage assessment

    Page(s): 13 - 16
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1630 KB) |  | HTML iconHTML  

    In this paper, we develop an approach for change detec tion in VHR imagery by using the discrete cosine transform (DCT) and dynamic time warping (DTW). The approach considers both contextual information and more accurate registration of two images. The proposed approach was evaluated by using panchromatic images with 0.5 spatial resolution taken from Worldview satellite, and Haiti earthquake-induced changes were detected. A small region was selected for analysis where the Haiti palace was located, and the change map was visually compared to the change map obtained by using standard change detection tool of Erdas Imagine. View full abstract»

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  • Land cover classification by using multi-temporal COSMO-SkyMed data

    Page(s): 17 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1043 KB)  

    The objective of this paper is to report on the crop classification activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains. In particular, the objective of the classification activities is to assess the potential of multi-temporal series of X-band COSMO-SkyMed SAR data for crop classification. The selected agricultural site is located in the Capitanata plain close to the Foggia town (Puglia region, Southern Italy). Over this area, 8 Stripmap PingPong COSMO Sky-Med images at HH/HV polarization and at low incidence angle were acquired from April to August 2010. In the paper, a classification scheme based on the Maximum Likelihood algorithm is applied to the multi-temporal data set and its accuracy is assessed with respect to a reference map obtained by means of SPOT data. View full abstract»

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  • Monitoring crop growth inter-annual variability from MODIS time series: Performance comparison between crop specific green area index and current global leaf area index products

    Page(s): 21 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (724 KB) |  | HTML iconHTML  

    Optical remote sensing time series can be used to retrieve biophysical variables indicating crop status, such as leaf area index (LAI) or, more appropriately, green area index (GAI). If these variables are sensible to inter-annual seasonal variations, they can be of great value for crop growth monitoring, especially if they can be coupled with ecophysiological models using data assimilation. This study presents a multi-annual comparison between currently available global LAI products and crop specific GAI retrieved from MODIS 250 m imagery obtained by controlling pixel-target adequacy. This comparison is done over a region in Belgium with fragmented agricultural landscapes. Results indicate that, by assuring a crop specific information and smoothing information using thermal time, the GAI product has a higher sensitivity to the variability of growing conditions that may be encountered across the region, and thus out-performs the other LAI products. View full abstract»

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  • Dynamic mapping of cropland areas in Sub-Saharan Africa using MODIS time series

    Page(s): 25 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (994 KB) |  | HTML iconHTML  

    Mapping cropland areas in a dynamic way is of great interest to successfully monitor agricultural areas and food security. Existing cropland masks are either too coarse or inaccurate or are limited in spatial coverage. This study aims at developing a method for dynamic mapping of cropland areas in Sub-Saharan Africa and at producing a multi-annual map of cropland extent at 250m using MODIS time series. The originality of the approach consists of including a dynamic and automatic stratification that allows tuning the classification parameters according to the inter-annual variability, and exploiting the local differences of spectral signatures between natural vegetation and crops during the green-up season. The accuracy of the product is assessed using a large sample of points interpreted on high resolution images and is compared to the accuracy of two existing cropland maps. View full abstract»

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  • Monitoring land cover changes in Hulun Buir by using object-oriented method

    Page(s): 29 - 32
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (918 KB) |  | HTML iconHTML  

    The grassland in China occupies more than 40% of its rural land area. However, grassland degradation has been a serious problem in recent years. Thus, a policy of returning cultivated land into grassland is enacted. An object-oriented image classification using different feature objects was adopted to classify grassland and a hierarchy of layers in different years for change detection was deployed in this paper to monitor land cover changes. An experiment was conducted in Hulun Buir Meadow in Inner Mongolia, China. The experiment shows that the accuracy of classification obtained by the object-oriented method is much higher than that of the traditional unsupervised ISODATA classification. Grassland protection action is taking effect maintaining a sustainable use of grassland ecosystem. View full abstract»

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  • Clustering analysis applied to NDVI/NOAA multitemporal images to improve the monitoring process of sugarcane crops

    Page(s): 33 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1117 KB) |  | HTML iconHTML  

    This paper discusses how to take advantage of clustering techniques to analyze and extract useful information from multi-temporal images of low spatial resolution satellites to monitor the sugarcane expansion. Additionally, we introduce the SatImagExplorer system that was developed to automatically extract time series from a huge volume of remote sensing images as well as provide algorithms of clustering analysis and geospatial visualization. According to experiments accomplished with spectral images of sugarcane fields, this proposed approach can be satisfactorily used in crop monitoring. View full abstract»

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  • Automatic interpolation of phenological phases in Germany

    Page(s): 37 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (832 KB) |  | HTML iconHTML  

    The German joint project DeCover 2 is developing a methodological framework to cope with the increasing demand for up-to-date land cover information using remote sensing techniques. New satellite systems like RapidEye provide both data of high geometric resolution and high repetition rates. Because of the Germany-wide diversity of natural conditions, same acquisition dates don't correspond to same phenological phases. Thus, a phenological structuring of the available imagery over the year is needed for the assessment of Rapid-Eye imagery regarding their suitability for the classification and distinction of vegetation classes. On the example of the phenological phase `Yellow Ripeness' of Winter Wheat in 2010, the presented algorithm demonstrates for the total area of Germany how daily phenological phases can be automatically interpolated on demand, in real-time and considering interpolation accuracies. As input, daily provided point data on temperature and phenological phases from the extensive network of the German Weather Service as well as a SRTM digital elevation model are used. The modeling results enable the identification of temporal phenological windows for specific test sites. View full abstract»

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  • A robust approach for phenological change detection within satellite image time series

    Page(s): 41 - 44
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1116 KB) |  | HTML iconHTML  

    The majority of phenological studies have focussed on extracting critical points, i.e. phenological metrics such as start-of-season, in the seasonal growth cycle. These metrics do not exploit the full temporal detail of time series, depend on their definition or threshold, and are influenced by disturbances. Here, we evaluated a robust phenological change detection ability of a method for detecting abrupt, gradual, and phenological changes within time series. BFAST, Breaks For Additive Seasonal and Trend method, integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within trend and seasonal (i.e. phenology) component. We tested BFAST by analysing 16-day MODIS NDVI composites (MOD13C1 collection 5) between 2000-2009 covering Australia. This illustrated that the method is able to detect the timing of major phenological changes within time series while accounting for abrupt disturbances and gradual trends. It was also shown that the phenological change detection is influenced by the signal-to-noise ratio of the time series. The BFAST method is a generic change detection method which can be applied to any time series data. The methods are available in the BFAST package for R from CRAN (http://CRAN.R-project. org/package=bfast). View full abstract»

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  • PhenoSat — A tool for vegetation temporal analysis from satellite image data

    Page(s): 45 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (657 KB) |  | HTML iconHTML  

    The availability of temporal satellite image data has increased considerably in recent years. A number of satellite sensors currently observe the Earth with high temporal frequency thus providing a tool for monitoring/understanding the Earth-surface variability more precisely, for several applications such as the analysis of vegetation dynamics. However, the extraction of vegetation phenology information from Earth Observation Satellite (EOS) data is not easy, requiring efficient processing algorithms to properly handle the large amounts of data gathered. The purpose of this work is to present a new, easy-to-use software tool that produces phenology information from EOS vegetation temporal data - PhenoSat. This paper describes PhenoSat, focusing on two new features: the determination of the beginning and maximum of a double growth season, and the selection of a temporal sub-region of interest in order to reduce and control the data evaluated. View full abstract»

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  • Assessing the impact of the orbital drift of SPOT-VGT1 by comparing with SPOT-VGT2 data

    Page(s): 49 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    This paper presents an on-going study on the impact of the orbital drift of VGT1 using concurrent images from VGT2, which is still orbiting within the mission specifications. The paper elaborates on the methodology and presents a few preliminary results. View full abstract»

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  • Analytical description of pseudo-invariant features (PIFs)

    Page(s): 53 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (958 KB) |  | HTML iconHTML  

    Invariant features are needed for atmospheric normalization of images pairs. Powerful statistical approaches now exist, designed to isolate unchanged pixels based on quantitatively evaluating the spectral correlation of pixels in image pairs. This suggests that it should be possible to reach similar results following an analytical path, and our hypothesis is that the derivation of an analytical procedure will yield some physical insight that is not directly accessible with a stochastic approach. In this paper we derive an analytical formula that relates PIFs to the radiometric properties of the scenes. The formula is then inverted to yield an estimate of the ratio of transmission spectra of the two images given the path radiance for each scene and a set of invariant features. View full abstract»

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  • Active-learning based cascade classification of multitemporal images for updating land-cover maps

    Page(s): 57 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (974 KB) |  | HTML iconHTML  

    This paper presents a novel active-learning (AL) technique in the context of the cascade classification of multitemporal remote-sensing images for updating land-cover maps. The proposed AL technique is based on the selection of unlabeled samples that have maximum uncertainty on their labels assigned by cascade classification, and explicitly exploits temporal correlation between multitemporal images. Uncertainty of samples is assessed by conditional entropy that is defined on the basis of class-conditional independence assumption in time domain. The proposed conditional entropy based AL method for cascade classification technique is compared with a marginal entropy based AL technique adopted in the context of single-date image classification. Experimental results obtained on two multispectral and multitemporal data sets show the effectiveness of the proposed technique. View full abstract»

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  • Multi-temporal damage assessment of linear infrastructural objects using Dynamic Bayesian Networks

    Page(s): 61 - 64
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (867 KB) |  | HTML iconHTML  

    In this paper, a Dynamic Bayesian Network (DBN) is presented which assesses infrastructural objects concerning their functionality after natural disasters. The presented model combines multi-temporal observations from remote sensed images with simulations based on Digital Elevation Models (DEM). The inference in the DBN is established using the sum-product algorithm. The improved performance of DBN is shown compared to simpler pixel-based and topology-based graphical models. The paper shows results of the model assessing roads concerning their trafficability after flooding. In addition, an evaluation of the results with a reference is conducted. View full abstract»

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  • Spatiotemporal dimensionality and time-space characterization of vegetation phenology from multitemporal MODIS EVI

    Page(s): 65 - 68
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3679 KB) |  | HTML iconHTML  

    Spatiotemporal dimensionality refers to the structure of the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to an approach for representing this continuum as combinations of spatial and temporal components with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling - both deterministic and statistical. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. Empirical Orthogonal Function analysis, used in conjunction with Temporal Mixture Models, provide a way to 1) Represent the spatiotemporal dimensionality of an image time series, 2) Identify distinct temporal modes and their spatial distributions, and 3) Map the relative contributions of these modes to the observed image time series as spatially continuous fields. Some strengths and limitations of Time-Space characterization are illustrated using multitemporal MODIS EVI time series of vegetation dynamics on the Ganges-Brahmaputra delta. View full abstract»

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  • Clustering of satellite image time series under Time Warping

    Page(s): 69 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1440 KB) |  | HTML iconHTML  

    Satellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions which aim at providing a coverage of the Earth every few days with high spatial resolution. In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling and one will need to compare irregularly sensed time series. In this paper, we present an approach to satellite image time series analysis which is able to both deal with irregularly sampled series and to capture distorted behaviors. We present the Dynamic Time Warping from a theoretical point of view and illustrate its abilities for satellite image time series clustering. View full abstract»

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  • Effects of multitemporal scene changes on pansharpening fusion

    Page(s): 73 - 76
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1478 KB) |  | HTML iconHTML  

    Goal of this work is to investigate the effects of temporal misalignments between multispectral (MS) and panchromatic (Pan) observations when they are fused together to yield a pansharpened product. Conversely from the case in which spatial misalignments are present between MS and PAN images, for which the performances of component substitution (CS) fusion methods are recognized better than multiresolution analysis (MRA) schemes, both quantitative and qualitative results show that multitemporal misalignments are better compensated by MRA rather than by CS methods. View full abstract»

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  • Low and high spatial resolution time series fusion for improved land cover map production

    Page(s): 77 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (870 KB) |  | HTML iconHTML  

    In the coming years, several optical space-borne systems with high resolution, high temporal frequency revisit and constant viewing angles will be launched. The availability of these data opens the opportunity for the development of new applications which require to closely monitor the temporal trajectory of the characteristics of land surfaces. However, due to cloud cover and even to some rapid changes, a higher temporal resolution may be needed for some applications. One of the ways to improve the temporal resolution for these satellites is to merge their data with higher temporal resolution systems. For now, these other systems will fatally have a lower spatial resolution or a limited field of view. The goal of our work is to assess the usefulness of image fusion techniques for the joint use of Proba-V/Sentinel-3 data and Venus/Sentinel-2 images for land-cover monitoring. We are interested in the generation of land-cover maps and time profiles of surface reflectances with a spatial resolution of 10 to 30 m. with an update frequency of about 10 days. View full abstract»

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