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We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neural networks, we develop an end-to-end network that removes the extremely high-level s&p noise without performing any non-trivial preprocessing tasks, which is different from all the existing literature in s&p noise removal. Experiments show that inserting median layers into a simple fully-convolutional network with the L2 loss significantly boosts the signal-to-noise ratio. Quantitative comparisons testify that our network outperforms the state-of-the-art methods with a limited amount of training data. The source code has been released for public evaluation and use (https://github.com/llmpass/medianDenoise).

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.

International audience; Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.

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.

Connective segmentation based on the definition of a dissimilarity measure on pairs of adjacent pixels is an appealing framework to develop new hierarchical segmentation methods. Usually, the dissimilarity is fully determined by the intensity values of the considered pair of adjacent pixels, so that it is independent of the values of the other image pixels. In this paper, we explore dissimilarity measures depending on the overall image content encapsulated in its local mutual information and show its invariance to information preserving transforms. This is investigated in the framework of the connective segmentation and constrained connectivity paradigms and leads to the concept of dependent connectivities. An efficient probability estimator based on depth functions is proposed to handle multi-dimensional images. Experiments conducted on hyper-spectral and multiangular remote sensing images highlight the robustness of the proposed approach., JRC.G.2-Global security and crisis management

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.

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

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 ...

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.

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

Satellite imagery is a valuable source of information for assessing damages in distressed areas undergoing a calamity, such as an earthquake or an armed conflict. However, the sheer amount of data required to be inspected for this assessment makes it impractical to do it manually. To address this problem, we present a semi-supervised learning framework for large-scale damage detection in satellite imagery. We present a comparative evaluation of our framework using over 88 million images collected from 4, 665 KM2 from 12 different locations around the world. To enable accurate and efficient damage detection, we introduce a novel use of hierarchical shape features in the bags-of-visual words setting. We analyze how practical factors such as sun, sensor-resolution, satellite-angle, and registration differences impact the effectiveness our proposed representation, and compare it to five alternative features in multiple learning settings. Finally, we demonstrate through a user-study that our semi-supervised framework results in a ten-fold reduction in human annotation time at a minimal loss in detection accuracy compared to manual inspection

The location and identification of human settlements is a key information in any assessment related to human security and safety decision process, from the preparedness to and mitigation of natural hazards to postdisaster response and reconstruction. The human settlements can be defined as infrastructures build on earth surface for living, trading or any social activity. In order to cover and analyze large areas where scarce and heterogeneous data may exist, the automatic information extraction from Earth Observation is the only sustainable strategy. Several studies about the detection of built-up presence from high resolution satellite were performed during the past few years. Among the various methods, a built-up index aggregating anisotropic textural co-occurence measures (PanTex) was demonstrated to be a robust indicator, where 54 high resolution optical images representing cities distributed around the world are processed. The validation of the built-up presence index emphasized its robustness against the type of constructions, the sun angles and the seasons of acquisitions. In this paper, we propose an alternative procedure for the calculation of built-up presence index from panchromatic high resolution satellite images. While radiometric descriptors of human settlements are highly variable across the world and with illumination conditions, the shape of settlements often contains right angles. This property being more stable, we propose a built-up index based on the density of corners. The index is obtained in two steps: - corners are detected by multi scale Harris detector based on differential morphological decompositions; - corners are spatially aggregated to form a density of corners, which is the built-up index. The differential morphological decomposition is a scale-space representation of the image, where image elements are separated by their scales. Then, the Harris corner indicator, which is highly dependent on a scale parameter, can be adapted to a set of scales. The output of the corner detection is a set of points associated to a scale, which represents the right angles in the image. The corners associated to a scale in the range of admissible settlements dimensions are selected and spatially aggregated to derive a density of corners. The proposed index is extracted from various high resolution panchromatic images and it is compared to the PanTex. In the experimental section, the high correlation between both indicators proves the suitability of the proposed method for consistently detecting built-up presence. Moreover, it gives a new interpretation of the PanTex which is close to a density of corners. Such an observation is critical for understanding the variablity of the PanTex index in between dense urban and industrial areas (industrial areas being composed of large buildings mechanically contain less corners)., JRC.G.2-Global security and crisis management

A new compact representation of differential morphological profile (DMP) vector fields is presented. It is referred to as the CSL model and is conceived to radically reduce the dimensionality of the DMP descriptors. The model maps three characteristic parameters, namely scale, saliency and level, into the RGB space through a HSV transform. The result is a a medium abstraction semantic layer used for visual exploration, image information mining and pattern classification. Fused with the PANTEX built-up presence index, the CSL model converges to an approximate building footprint representation layer in which color represents building class labels. This process is demonstrated on the first high resolution (HR) global human settlement layer (GHSL) computed from multi-modal HR and VHR satellite images. Results of the first massive processing exercise involving several thousands of scenes around the globe are reported along with validation figures., JRC.G.2-Global security and crisis management

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

In this paper, mixed information similarity measure and a multidimensional density estimation method based on multivariate Edgeworth series expansion are proposed and assessed for the task of multi-temporal change detection. To unify mutual information and variational information, mixed information is proposed to quantify the degree of dependence between two random variables, which are intuitively appropriate for multi-temporal change detection. In the literature, Edgeworth series expansion is widely used in statistics and various engineering fields for one-dimensional density estimation. To compute the mixed information measure, multidimensional density estimation based on multivariate Edgeworth series expansion is proposed and evaluated. Two experiments on real SAR images and optical images are carried out to evaluate the performance of change detection. Experimental results confirm the promising capability of mixed information and the multivariate density estimation based on Edgeworth series expansion., JRC.DG.G.2-Global security and crisis management

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

In this paper, a novel method for multi scale corner analysis and detection is presented. First, stateof-the-art Harris–Laplace corner detector is reminded, which benefits from linear scale-space analysis. Secondly, a non-linear scale-space transform, namely Differential Morphological Decomposition, is described. This multi-scale transform is used jointly with the Harris corner indicator to build a new multi scale corner detector. Both corner detectors are visually assessed on synthetic and satellite images, highlighting the advantages of such a method, JRC.G.2-Global security and crisis management

In this paper, we propose a novel approach for breaking up an image into multi-scale overlapping objects. The image is decomposed by granulometry or differential morphological pyramid, resulting in a discrete scale-space representation. Then, the scale-space transform is segmented by a region based method. Projecting the obtained scale-space partition into space constitutes the disintegrated image representation, which enables a multi-scale object based image description., JRC.DG.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