Search

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.

With the increased spatial resolution of current sensor constellations, more details are captured about our changing planet, enabling the recognition of a greater range of land use/land cover classes. While pixeland object-based classification approaches are widely used for extracting information from imagery, recent studies have shown the importance of spatial contexts for discriminating more specific and challenging classes. This paper proposes a new compact representation for the fast query/classification of compound structures from very high resolution optical remote sensing imagery. This bag-of-features representation relies on the multiscale segmentation of the input image and the quantization of image structures pooled into visual word distributions for the characterization of compound structures. A compressed form of the visual word distributions is described, allowing adaptive and fast queries/classification of image patterns. The proposed representation and the query methodology are evaluated for the classification of the UC Merced 21-class data set, for the detection of informal settlements and for the discrimination of challenging agricultural classes. The results show that the proposed representation competes with state-of-the-art techniques. In addition, the complexity analysis demonstrates that the representation requires about 5% of the image storage space while allowing us to perform queries at a speed down to 1 s/ 1000 km 2 /CPU for 2-m multispectral data.

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.

Roofless buildings are encountered in case of conflicts and disasters as well as construction sites. A methodology for characterizing and counting roofless buildings in very high resolution optical images is presented. Using morphological transform to extract specific image components, the proposed method spatially aggregates them in a fuzzy logic framework. The result is a map of roofless building membership. This membership map is then used with partial ground truth to estimate the total number of roofless buildings. Finally, the methods are validated with a WorldView panchromatic image, where the membership map and the counting estimate show good accuracies., JRC.G.2-Global security and crisis management

A low-complexity spectrum-sensing method that is based on dyadic (binary) tree partitioning is proposed. For this purpose, first, a representation of the spectrum occupation with a dyadic tree is developed. Then, the accuracy of this representation is evaluated through a rate-distortion criterion. Using this criterion, a pruning algorithm for the dyadic tree that minimizes the number of sensing operations for a predetermined value of sensing accuracy is conceived. After tailoring the pruning algorithm to the missed-detection and collision requirements of the opportunistic access, an adaptive version that performs efficient sensing in slowly varying environments is proposed. The proposed algorithm is tested on real spectrum measurements taken in the 400-910-MHz range. The sensitivity of the proposed algorithm to sensing imperfections is also evaluated. The obtained results demonstrate that the proposed algorithm provides a mean distortion of 11.4% and a mean collision probability of 5.6% for sensing only 30% of the total spectrum. A comparison with conventional energy-based detection highlights a gain of 50% in terms of the number of required measurements per channel in the low-signal-to-noise-ratio (SNR) region, where conventional energy detection is known to have poor detection performance.

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.

The cognitive radio paradigm is based on the ability of sensing the radio environment in order to make informed decisions. This paper describes the effects of sensing on the cognitive radio channels capacity region. Sensing is modeled as a compression channel, which results in partial knowledge of the primary messages at the cognitive transmitter. This model enables to impose constraints on the sensing strategy. First, the dirty paper channel capacity is derived when the channel encoder knows partially the side information. Then, the capacity area of the Gaussian cognitive channel with partial information is derived. Finally, numerical results illustrate the capacity reduction associated with constrained sensing, in comparison to the capacity of the cognitive radio channel.

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

Very High Resolution (VHR) satellite images are products which are very useful for assessing humanitarian situations following a disaster or a crisis. When disaster or conflict hits, generally several square kilometers on Earth surface are involved. Having a high resolution and an extended surface, the scenes, from which the information is extracted, become huge in the number of pixels. Built-up structures are the first visible objects from these images which give information about the situation. In this paper, an interactive image mining paradigm is presented for detecting structures of interest in gigapixel images. The proposed approach is tested for refugee tents detection in submetric panchromatic image., JRC.G.2-Global security and crisis management

Satellite Multitemporal Observations and Satellite Image Time Series are a new type of data sets acquired with optical and Synthetic Aperture Radar (SAR) technologies. Presently, due to the large number of Earth Observation satellites, every location on Earth can be observed very often; however, the observations in practical situations are usually multisensor. As a result, new models and algorithms are needed to automatically perform the data analysis. This paper presents two approaches for the analysis of multitemporal and multisensor data analysis. The first approach studies similarity measures derived from information measures and we assess their performance for multitemporal analysis. The temporal evolution of the scene changes is measured as a variation in the common and disjoint information measures between the different sensor data models. The second approach proposes the use of specific density estimation methods to reduce approximations effects for the detection of the multisensory changes. Usually the problem of change detection can be decomposed into two steps [1]: the generation of a change map based on the comparison of the 2 images using a similarity function, and a change classification performed either in un-supervised or supervised schema. In the former situation certain kinds of prior information about the scene, such as prior distribution assumption mode, is necessary. This paper will focus on an unsupervised approach of the generation of the indicator image in order to produce the binary change map. This approach is based on the comparison of local statistical similarity measures. In the literature, there several kinds of similarity measure, among which mutual information based similarity measures, have been proved to be more efficient. In [2], have been introduced several similarity measures between multi-sensor images. These measures are based on concepts such as statistical dependence or mutual information. The use of these measures allows for the design of image registration algorithms and automatic change detection techniques. And also in [3] are presented a new approach based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to compare the performance of each similarity measures. And he concludes the CRA and the mutual information measures perform the best. Taking advantage of the Mutual Information, in (5) is proposed a pixel-based approach comparing the localized Mutual Information shared by two pixels. When the two pixels share a lot of Mutual Information, their location is considered unchanged. From this idea, in (4) it was introduced a new informational measure derived from the Mutual Information: the Mixed Information. This new informational measure quanti¿es in a unique framework the shared and different informations such that time separated pixels with different informations are set changed. The new method based on Mixed Information was also proffed to be invariant to nonlinear changes, thus we further investigate ist use for multi-sensor cahnge detectin. Both the methods of in [3] and [4] are based on distribution estimation as histograms. In [1] it is suggested that the histogram method should be avoided due to the need of a high number of samples for estimation. As alternative, a cumulant-based density estimation is used to derive the local distribution which is furtehr used to compare the similarity between two images with the Kullback-Leibler divergence. The marginal distributions of each image are used To overcome the shortcomings, this paper presents an alternative scheme which is an extension of the work in [4]., JRC.DG.G.2-Global security and crisis management