Description: In this paper, the link-based cluster ensemble (LCE) method is utilized to improve cloud classification and satellite precipitation estimation. High resolution Satellite Precipitation Estimation (SPE) is based on the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. This modified SPE with the incorporation of LCE involves the following four steps: 1) segmentation of infrared cloud images into patches; 2) cloud patch feature extraction; 3) clustering cloud patches using LCE; and 4) dynamic application of brightness temperature (Tb) and rain-rate relationships, derived using satellite observations. In order to cluster the cloud patches, the LCE method combines multiple data partitions from different clustering methods. The results show that using the cluster ensemble increases the performance of rainfall estimates compared to the SPE algorithm using a Self Organizing Map (SOM) neural network. The false alarm ratio (FAR), probabilities of detection (POD), equitable threat score (ETS), and bias are used as quantitative measures to assess the performance of the algorithm. It is shown that both the ETS and bias provide improvement in the summer and winter seasons. Almost 5% ETS improvement is obtained at some threshold values for the winter season using the cluster ensemble.

Description: This paper presents a novel method for segmenting the oil spill regions in the SAR satellite images taken in broad daylight using illumination-reflectance based level set model. These images of oil spills taken in broad daylight appear as a blend of dark areas with scintillations of glitter due to the illumination and reflectance components present. Most of the dark areas in the SAR images are the areas indicating oil spills because the oil dampens the capillary waves on the sea surface. The presence of the glitter induces speckle in SAR images. This does not only reduces the interpreter's ability to resolve fine detail, but also makes automatic segmentation of such images difficult. Segmentation of such images using conventional level set methods makes the process cumbersome and may lead to improper results. The accuracy of segmentation greatly depends on the amount of the illumination and reflectance (IR) components present in the images. To perform segmentation of such images we propose an adaptive level set evolution process based on the IR components in them. This can be achieved by combining a new signed pressure function which is derived from the amount illumination and reflectance present in the image. The IR components present in image are extracted by the process of homomorphic decomposition with the help of filters with specific cut off frequencies. This method is the first application successfully implemented on SAR images and the results are found to be superior when compared with earlier techniques. Comparative analysis is made with the conventional region based level sets in terms of accuracy of segmentation for complex images.

Description: In this paper, a road network grouping algorithm for Synthetic Aperture Radar (SAR) images is proposed by exploiting multiscale geometric analysis of detector responses. Before running the algorithm, a response map made up of responses, which is binarized, skeletonized, and vectorized to generate road candidates, is obtained by applying a local detector to a SAR image first. Then the proposed method identifies real road segments among the candidates and fills gaps between them. It works in three steps. 1) Guidance segments are extracted at different resolutions from the response map using multiscale techniques and merged to get a more appropriate approximation. 2) Segments are labeled “road” or “noise” using relaxation labeling techniques, among which “road” ones are grouped as they may lie on different roads. 3) Connection points between candidates are acquired by mapping candidates to grouped “road” guidance segments. Those connection points are linked with straight lines or curvilinear segments after a segmentation process. The experiments on TerraSAR images show the effectiveness of this new method.

Description: Remote sensing plays a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties. In this paper, we present an approach for supporting monitoring compliance of river networks in context of the European Water Framework Directive. Only a few approaches have been developed for extracting river networks from satellite data and usually they require manual input, which seems not feasible for automatic and operational application. We propose a method for the automatic extraction of river structures in TerraSAR-X data. The method is based on mathematical morphology and supervised image classification, using automatically selected training samples. The method is applied on TerraSAR-X images from two different study sites. In addition, the results are compared to an alternative method, which requires manual user interaction. The detailed accuracy assessment shows that the proposed method achieves accurate results (Kappa $ {sim}$ 0.7) and performs almost similar in terms of accuracy, when compared to the alternative approach. Moreover, the proposed method can be applied on various datasets (e.g., multitemporal, multisensoral and multipolarized) and does not require any additional user input. Thus, the highly flexible approach is interesting in terms of operational monitoring systems and large scale applications.

Description: Mixed pixels are one of the largest sources of error and uncertainty in mapping from remotely sensed data. A Hopfield neural network based approach to super-resolution mapping has become popular for mapping at a sub-pixel scale, partly because it seeks to maintain the class proportional information indicated by a soft classification analysis. The use of the approach is, however, handicapped by a lack of guidance on the parameter setting values and of the impacts of different landscape patterns on the analysis. Here, the sensitivity of the Hopfield neural network for super-resolution mapping is investigated with a focus on the effect of different landscape types and parameter settings using simulated and real data sets. It is shown that the method's suitability varies between landscapes, being most suited to situations in which landscape patches are large (〉 1 pixel) . Additionally, for such landscapes the widely used scenario in which the weighting parameters are set at equal values is successful but the approach is less effective for the mapping of small isolated land cover patches. With the latter, it is shown to be important to weight the area constraint highly and undertake a large number of iterations. Critically, it is shown that equal weighted parameter settings and imbalanced settings to emphasize the area constraint are most suitable for landscapes comprising large and small patches respectively. Moreover, the positive attributes of these two sets of parameter settings may be combined to yield an enhanced mapping method for landscapes that comprise a mixture of patch sizes.

Description: The recently available Soil Moisture and Ocean Salinity (SMOS) 1.4 GHz based soil moisture retrievals for the year of 2010 and the first nine months of 2011 are assessed over the continental United States (CONUS) region, along with soil moisture retrievals produced at Princeton University based on the Advanced Microwave Scanning Radiometer (AMSR-E) 10.7 GHz channel using the Land Surface Microwave Emission Model (LSMEM) and in-situ measurements from the Natural Resource Conservation Service's (NRCS) Soil Climate Analysis Network (SCAN). The assessment is carried out using a performance metric developed by Crow (J. Hydromet., 2007), which calculates the ability of soil moisture estimates to correct errors in surface moisture predictions through a linear Kalman filter. Within the Crow framework, SMOS retrievals show the same level of skill as AMSR-E/LSMEM or SCAN when evaluated on the days where both are available. But the SMOS product is significantly less available than AMSR-E/LSMEM or SCAN, especially on rainy days, therefore it is less able to reproduce the rainfall-moisture dynamics and consequently achieves a lower performance metric if all available data are used from all products. Detailed analysis shows that, with uncertainties, the performance of both SMOS and AMSR-E/LSMEM generally decays with thicker vegetation and wetter climate but is not significantly influenced by topography. We expect SMOS to further improve its accuracy through validation studies and its availability under rainy conditions as well.

Description: Combining super-resolution techniques can increase the accuracy with which the shape of objects may be characterised from imagery. This is illustrated with two approaches to combining the contouring and pixel swapping methods of super-resolution mapping for binary classification applications. In both approaches, the output of the pixel swapping method is softened to allow a contour of equal class membership to be fitted to it to represent the inter-class boundary. The accuracy of super-resolution mapping with the individual and combined techniques is explored, including an assessment of the effect of variation in the number of neighbors and zoom factor on pixel swapping based analyses. When combined, the error with which objects of varying shape were represented was typically greatly reduced relative to that observed from the application of the methods individually. For example, the root mean square error in mapping the boundary of an aeroplane represented in relatively fine spatial resolution imagery decreased from 14.41 m with contouring and 4.35 m with pixel swapping to 3.07 m when the approaches were combined.

Description: How the heterogeneous and distributed atmospheric satellite sensors can achieve precise discovery and collaborative observation is a big challenge. In this study, we propose an atmospheric satellite sensor observation system meta-model that reuses and extends the existing geospatial or sensor-related metadata standards to enable the sharing and interoperability of atmospheric satellite sensors. The Open Geospatial Consortium Sensor Model Language (SensorML) has a clear hierarchy in describing the metadata framework, and it is adopted as the carrier to formalize our proposed meta-model into the Atmospheric Satellite Sensor Observation Information Model (A-SSOIM). Three different types of atmospheric satellite sensors are used to test the versatility of the proposed meta-model and the applicability of this formal expression of A-SSOIM. Results show that the proposed meta-model can be reused in all kinds of atmospheric satellite sensors to enable the sharing of atmospheric satellite sensor information and potentially promoting the interoperability of these satellite sensors.

Description: Santorini Volcano is an active strato volcano, located at the central part of the Hellenic Volcanic Arc, developing ad retro of the Hellenic Arc and Trench. The explosive history of the volcano dates back to 1645 BC with the Minoan eruption, while it is documented to have produced at least ten eruptions until 1950 AD. The most recent volcanic unrest began in early 2011. Multi-reference Synthetic Aperture Radar (SAR) Interferometric techniques were applied to study the evolution of ground deformation during 1992–2011, with the use of ERS-1 and -2 and ENVISAT radar imagery. Datasets of common acquisition geometry were added into a single stack so as to obtain the linear deformation rates by means of phase averaging. However, to reveal the deformation history of the volcano, Singular Value Decomposition (SVD) method was implemented. This allowed retrieving ground deformation time-series on a pixel basis over regions with high temporal coherence levels. Results from independent tracks, agreeing with each other, suggest a deformation rate of approximately 5 mm/yr of subsidence at the southern part of Nea Kammeni Volcano, for the period 1992–2010. For the unrest period of 2011, intense uplift of 4.8 cm was observed throughout Nea Kammeni. Global Positioning System (GPS) observations from a local geodetic network confirm the DInSAR findings.