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Accurate and topical spatial datasets representing human populations are foundational to solving humanitarian issues. This paper provides unique solutions to accurately map human settlements both at scale and across remote areas. A method of village boundary extraction from very high resolution optical satellite imagery is proposed. Furthermore, the supplement of a crowd-sourced validation process to filter the detections for higher accuracy and the automated mosaic techniques are detailed. To demonstrate the computational and informational scalability of the process, four distinct geographic locations in Nigeria, Somalia, Pakistan, and Afghanistan were analyzed for a total processed area of 592 000 km 2 , comprised of 1159 high-resolution DigitalGlobe images. The geographic variability of the locations and the scale of the projects required dynamic and automated solutions, respectively. The curated results exhibit high recall and precision of human settlement data in remote as well as urban areas. Crowdsourced validation allows complete control over the precision of the final village boundary layer, and given time, an effective 100% precision can be achieved. This highly scalable and precise system is perfectly adequate for processing regional and country-scale areas, with minimal human effort.

This chapter presents a selection of image representation methods for very high spatial resolution optical data acquired by agile satellite platforms. Each method aims at specific properties of the image information content and can be tailored to address unique features in spatial, temporal, and angular acquisitions. Techniques for the identification and characterization of surface structures and objects often employ spatial and spectral features best represented in panchromatic and multi-spectral images, respectively. In both cases, the vastness of the data space can only be addressed effectively by means of some data representation structure that organizes the image information content in meaningful ways. The latter suggest that a globally optimal representation of the object(s) of interest can be obtained through interactions with a scale space as opposed to single-scale information layer. Two examples, the Max-Tree and Alpha-Tree algorithms, are discussed in the context of interactive big data information mining. Optical and structural properties of the surface materials can be exploited by analyzing the tempo-angular domain by means of anisotropic decompositions, which rely on the availability of surface reflectance data and dense angular sampling. Bidirectional reflectance distribution functions of various materials are discussed in detail, showing that the temporal information should always be coupled to the corresponding angular component to make the best use of the available imagery.

A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. The analysis is performed by unsupervised change detection between built-up presence indicators extracted separately from the scenes. The obtained index has an effective resolution which is coarser than the Ground Sample Distance, thus allowing a good spatial match between the indicators. Then, a local Mixed Information change indicator is employed in order to capture the non-linear temporal behaviors. A region-based approach is developed in synergy with the local Mixed Information in order to process consistently large scenes which may be affected by regional acquisition distortions. Experiments are conducted on SPOT 5 scenes acquired in 2003 and 2008 which cover a suburban area (45×45 km2) of the city of Tangshan in China. The urbanization detector is validated by visual interpretation, giving an equal omission/commission probability of 10%. A comparison to linear change indicators highlights an improvement of 15% of the equal omission/commission probability.

A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

In advance of releasing a WorldView-3 (WV-3) dataset with both VNIR and SWIR bands for research purposes, this study was conducted to provide a baseline comparison of land use/land cover (LULC) classification based on hyperspectral and 16-, 8-, and 4-bands of WV-3 imagery. We chose a well-researched area over the city center of Pavia, Italy. Results suggest that the addition of spectral information from WV-3's SWIR bands helps bridge the gap between precision/recall scores obtained with multispectral VNIR vs. hyperspectral VNIR imagery.

This paper presents the results of the application of mathematical morphology for the automatic quantification of the number of tents and the detection of changes in the Menik Farm IDP camp in Sri Lanka. The tents were retrieved using an area-constraint top-hat opening applied to WorldView-1 data. The counting of tents was based on the centroids of the automatically detected structures indicating an overall number of 25,150 tents on the 26 June 2009. The comparison with a visual interpretation produced an R 2 of 0.97 with an error of 1.25%. In addition, an automated detection of changes inside a camp area was conducted. The comparison of the satellite image of 26 June 2009 (WorldView-1) and an image of 28 February 2010 (GeoEye-1) is based on mutual (mixed) information metric, after using morphological image processing techniques and previously specified criterion. Changes are observed on a terrain of around 15.2% of the total camp area and 3813 of previously detected structures disappeared in a period of ...

Earth observation image-understanding methodologies may be hindered by the assumed data models and the estimated parameters on which they are often heavily dependent. First, the definition of the parameters may negatively affect the quality of the analysis. The parameters could not be captured in all aspects, and those resulting superfluous or not accurately tuned may introduce nuisance in the data. Furthermore, the diversity of the data, as regards sensor type, spatial, spectral, and radiometric resolution, and the variety and regularity of the observed scenes make it difficult to establish enough valid and robust statistical models to describe them. This letter proposes algorithmic information theory-based analysis as a valid solution to overcome these limitations. We will present different applications on satellite images, i.e., clustering, classification, artifact detection, and image time series mining, showing the generalization power of these parameter-free data-driven methods based on the computational complexity analysis.

Satellite image time series (SITS) consist of a time sequence of high-resolution spatial data. SITS may contain valuable information, but it may be deeply hidden. This paper addresses the problem of extracting relevant information from SITS based on the information-bottleneck principle. The method depends on suitable model selection, coupled with a rate-distortion analysis for determining the optimal number of clusters. We present how to use this method with the Gauss-Markov random fields and the autobinomial random fields model families in order to characterize the spatio-temporal structures contained in SITS. Experimental results on synthetic data and SITS from SPOT demonstrate the performance of the proposed methodology

This paper proposes an experimental analysis of the Local Mutual Information (LMI) invariance in the context of unsupervised change detection between multi-spectral images. The conversion from Digital Numbers to Surface Reflectance does not affect theoretically the LMI based change map under the assumption of homogeneous atmospheric conditions. Experiments are conducted with QuickBird bi-temporal images to assess the robustness of the LMI to conversion to Surface Reflectance. Results show that the probability estimation, upon which the LMI depends on, is critical for maintaining invariant LMI based change detection values.

The enumeration of the population remains a critical task in the management of refugee/IDP camps. Analysis of very high spatial resolution satellite data proofed to be an efficient and secure approach for the estimation of dwellings and the monitoring of the camp over time. In this paper we propose a new methodology for the automated extraction of features based on differential morphological decomposition segmentation for feature extraction and interactive training sample selection from the max-tree and min-tree structures. This feature extraction methodology is tested on a WorldView-2 scene of an IDP camp in Darfur Sudan. Special emphasis is given to the additional available bands of the WorldView-2 sensor. The results obtained show that the interactive image information tool is performing very well by tuning the feature extraction to the local conditions. The analysis of different spectral subsets shows that it is possible to obtain good results already with an RGB combination, but by increasing the number of spectral bands the detection of dwellings becomes more accurate. Best results were obtained using all eight bands of WorldView-2 satellite., JRC.G.2-Global security and crisis management

A method for analyzing the urbanization process from multitemporal SPOT 5 panchromatic images is presented. A region-based local Mutual Information change indicator is proposed to perform the change analysis of large scenes. Experiments are conducted for mapping the urbanization of Tangshang, China, in between 2003 and 2008. The results show the efficiency of the change detection method., JRC.G.2-Global security and crisis management

This paper addresses the problem of building an index of compressed object databases. We introduce an informational similarity measure based on the coding length of two part codes. Then, we present a methodology for compressing the database by taking into account interobject redundancies and by using the informational similarity measure. The method produces an index included in the code of the data volume. This index is built such that it contains the minimal sufficient information to discriminate the data-volume objects. Then, we present an optimal two-part coder for compressing spatio-temporal events contained in satellite image time series (SITS). The two-part coder allows us to measure similarity and then to derive an optimal index of SITS spatio-temporal events. The resulting index is representative of the SITS information content and enables queries based on information content.