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Primary systemic - or neoadjuvant - chemotherapy (PST) is the standard of care in locally advanced breast cancer and it has also become an option in primary operable disease for patients who are candidates for adjuvant systemic chemotherapy. There are several advantages of administering PST: tumor downstaging--improving the chance of breast conserving surgery; in vivo assessment of tumor sensitivity to the chosen therapeutic regimen; and, early control of micrometastatic disease. On the other hand, the rate of tumor response can be used as a surrogate prognostic marker and for rapid screening of efficiency of new drugs. PST initially referred to systemic chemotherapy, but in recent years endocrine--and now multiple targeted therapies--are available in most of the countries within the confines of clinical trials.

As a consequence of the wide-spread application of digital geo-data in Geoinformation Systems (GIS), quality control has become increasingly important. A high degree of automation is required in order to make quality control efficient enough for practical application. In order to achieve this goal we have designed and implemented a semi-automatic technique for the verification of cropland and grassland GIS objects using 1 m pan-sharpened multispectral IKONOS imagery. The approach compares the GIS objects and compares them with data derived from high resolution remote sensing imagery using image analysis techniques. Textural, structural, and spectral features are assessed in a classification based on Support Vector Machines (SVM) in order to check whether a cropland or grassland object in the GIS is correct or not. The approach is explained in detail, and an evaluation is presented using reference data. Both the potential and the limitations of the system are discussed. German Federal Agency for Cartography and Geodesy (BKG)

In this article we present a method that extracts plantations from satellite imagery by finding and exploiting appropriate feature space projections. Segmentation is done using an automatic two-region segmentation based on the level set method. The behaviour of this algorithm is defined by a statistical region model that describes the similarity of regions using distances in arbitrary feature spaces. Subsequently different feature spaces will be evaluated regarding their plantation classification quality in an automatic fashion. The segmentation quality of our method is assessed by testing several orthophotos depicting a wide range of landscape types and comparing them with a manual segmentation. We show that a combination of simple texture based features like the structure tensor and the Hessian matrix are sufficient to facilitate an effective plantation segmentation scheme. BKG

http://www.uni-tuebingen.de/geo/met-map/index.html

Land cover classification plays a key role for various geo-based applications. Numerous approaches for the classification of settlements in remote sensing imagery have been developed. Most of them assume the features of neighbouring image sites to be conditionally independent. Using spatial context information may enhance classification accuracy, because dependencies of neighbouring areas are taken into account. Conditional Random Fields (CRF) have become popular in the field of pattern recognition for incorporating contextual information because of their ability to model dependencies not only between the class labels of neighbouring image sites, but also between the labels and the image features. In this work we investigate the potential of CRF for the classification of settlements in high resolution satellite imagery. To highlight the power of CRF, tests were carried out using only a minimum set of features and a simple model of context. Experiments were performed on an Ikonos scene of a rural area in Germany. In our experiments, completeness and correctness values of 90% and better could be achieved, the CRF approach was clearly outperforming a standard Maximum-Likelihood-classification based on the same set of features.

Since the first demonstration of the potential of the differential SAR interferometry in the early 1990s a lot of effort has been made to accurately estimate ground deformation with imaging radar sensors. This led to the invention of the Persistent Scatterer Interferometry (PSI) in the late 1990s. PSI enables the estimation of ground deformation for a set of temporally stable radar reflectors, the so called PS, with millimeter accuracy. The main advantage compared to methods commonly used for ground deformation monitoring like GPS is the possibility to cover large areas very economically. One of the main drawbacks is the opportunistic sampling of the target area, which is mainly governed by the distribution of stable radar reflectors within the scene. Besides problems caused by undersampling the main issue is due to the face, that the real world feature related to a PS is usually not known. This makes the interpretation of the results particularly difficult. While the assignment of these real world features is very difficult in the case of ERS like sensors, modern high resolution SAR sensors like TerraSAR-X (TSX) render this task possible. We investigate the use of SAR simulation to match real world features with PS extracted from a TSX stack acquired over the city of Berlin Germany. The simulation is based on a 3D city model of the area around the Potsdamer Platz, Berlin. National Natural Science Foundation of China/60950110351

Ecosystems provide several services for human well-being. The quality of these ecosystem services is among others affected by agriculture, the main land user in Europe. Thus, it is essential to provide information about land use in agricultural areas. In this research, land use classification of agricultural areas is carried out based on high-resolution Spotlight TerraSAR-X images (TSX-images) of two different polarisations (HH and VV). A stack of several radar images taken during the vegetation season is used for multi-temporal classification of land cover. The typical phenology of agricultural vegetation types and their individual phenological development during the year cause differences in the backscatter of the radar signal over time. Two different study areas are investigated, one in the North East of the city of Hannover, Germany called "Fuhrberger Feld" and one in the "Gorajec area" in the very South East of Poland. These two areas represent extremely diverse European regions with regard to agro-technological level, population density, cultivation form as well as geological and geomorphological conditions. Thereby, the radar signal backscatter for different regions is tested. Preliminary results show significant differences in the backscatter of crop types in SAR data of about 3 m, especially for grasslands, grain and broad-leaved crops. Furthermore the VV polarised radar signal has clearly lower backscattering for grains during summertime and for grasslands in general than for broad-leaved crops.

In the access part of communication networks user access rates are usually limited by technology and are much lower than the bottleneck link transmission capacity carrying the traffic flows aggregated. A possible model for bandwidth sharing of the bottleneck link is the Discriminatory Processor Sharing (DPS) models, in which the server capacity (link bandwidth) is distributed among different classes of users in an unequal manner. Recently, some DPS variants incorporating the access rate limits of users have been analyzed. These models are not bandwidth sparing in a sense, that the capacity share of a class may simply be cut at its access rate limit, and the incidentally residual bandwidth is not reused in other classes. In this paper we introduce and analyze a novel variant of DPS in which the original processor sharing effect and the access rate limit constraints are combined in a bandwidth economical way resulting a truly capacity-conserving operation. Besides the state space characterization of this model, two asymptotic behaviors are also presented. We also argue in the favor of practical significance of these asymptotics, that is it could greatly help in finding high quality approximate solutions of this DPS system, i.e.q in terms of the average waiting times of flows.

International audience; Satellite images with high spatial resolution raise many challenging issues in image understanding and pattern recognition. First, they allow measurement of small objects maybe up to 0.5 m, and both texture and geometrical structures emerge simultaneously. Second, objects in the same type of scenes might appear at different scales and orientations. Consequently, image indexing methods should combine the structure and texture information of images and comply with some invariant properties. This paper contributes to the indexing of high-resolution satellite images. We suggest a satellite image indexing method relying on topographic maps (Caselles et al., 1999) and a shape-based image indexing scheme (Xia et al., 2009). The proposed approach contains both the textural and structural information of satellite images and is also robust to changes in scale, orientation and contrast. Experimental analysis on a real satellite image database confirms the efficiency of the approach.

Rice is the staple food in Iran. More than 80 percent of rice area is distributed in the two northern provinces of Mazandaran and Gilan, so that investment in increasing the quantity and quality can impact an effective role in economic independence and sustainable agriculture. Increased efficiency in rice production is possible through varietal technology, advances in yield enhancement, and the successful development of hybrid technology. Nondestructive methods such as study the spectral reflectance of rice fields is a reliable way in remote sensing study. In this study we tested the possibility to predict highyielding rice varieties based on the spectral reflectance data in the red edge position (REP).Spectral reflectance of rice canopies from 350 to 2500 nm were acquired under clear sky in rice filed. The obtained results indicate that REP of Hybrid, Tarom, Neda and Khazar varieties are at longer wavelength, so they are predicted as more productive rice varieties