This letter proposes a nonlinear version of the eigenspace separation transform (EST) for subspace anomaly detection in hyperspectral imaging. The EST is defined in terms of the eigenvectors of the difference correlation matrix (DCOR) obtained using the data from the two classes. Using ideas found in the machine learning literature (i.e., the kernel trick), a nonlinear version-kernel EST (KEST)-is achieved by expressing the DCOR in terms of dot products in feature space and replacing all dot products with a Mercer kernel function that is defined in terms of input data space. Experimental results indicate that KEST outperforms many other commonly used subspace anomaly detection algorithms. 相似文献
Journal of Geographical Systems - As the volume, accuracy and precision of digital geographic information have increased, concerns regarding individual privacy and confidentiality have come to the... 相似文献
Rhodium, one of the least abundant of the Pt-group elements in the earth's crust, has the second highest concentration in seawater. This may be a consequence of strong complexing, perhaps kinetically restrained with time constants of the order of centuries to millenia. The element shows a nutrient-like distribution in waters of the Pacific Ocean. Pelagic and coastal sediments have Rh concentrations close to crustal values. There is a remarkable enrichment of Rh in ferromanganese minerals that cannot be explained by oxidative capture. Unlike the other Pt-group elements, Rh is enriched in the high temperature hydrothermal sulfide deposits as well as phosphorites. 相似文献
At COP21 in Paris, governments reiterated the importance of ‘non-Party’ contributions, placing big bets that the efforts of cities, regions, investors, companies, and other social groups will help keep average global warming limited to well under 2°C. However, there is little systematic knowledge concerning the performance of non-state and subnational efforts. We established a database of 52 climate actions launched at the 2014 UN Climate Summit in New York to assess output performance – that is, the production of relevant outputs – to understand whether they are likely to deliver social and environmental impacts. Moreover, we assess to which extent climate actions are implemented across developed and developing countries. We find that climate actions are starting to deliver, and output performance after one year is higher than one might expect from previous experiences with similar actions. However, differences exist between action areas: resilience actions have yet to produce specific outputs, whereas energy and industry actions perform above average. Furthermore, imbalances between developing and developed countries persist. While many actions target low-income and lower-middle-income economies, the implementation gap in these countries remains greater. More efforts are necessary to mobilize and implement actions that benefit the world’s most vulnerable people.
Policy relevance
Climate actions by non-state and subnational actors are an important complement to the multilateral climate regime and the associated contributions made by national governments. Although such actions hold much potential, we still know very little about how they could deliver in practice. This article addresses this knowledge gap, by showing how 52 climate actions announced at the UN Climate Summit in 2014 have performed thus far. Based on our analysis, we argue that the post-Paris action agenda for non-state and subnational climate action should (1) find more effective ways to incentivize private sector actors to engage in transnational climate governance through actions that seek to reduce greenhouse gas emissions and promote climate resilience in a tangible manner; (2) identify factors underlying effectiveness, to take appropriate measures to support underperforming climate actions; and (3) address the large implementation gap of climate actions in developing countries. 相似文献
Geocoding systems typically use more than one geographic reference dataset to improve match rates and spatial accuracy, resulting in multiple candidate geocodes from which the single “best” result must be selected. Little scientific evidence exists for formalizing this selection process or comparing one strategy to another, leading to the approach used in existing systems which we term the hierarchy‐based criterion: place the available reference data layers into qualitative, static, and in many cases, arbitrary hierarchies and attempt a match in each layer, in order. The first non‐ambiguous match with suitable confidence is selected and returned as output. This approach assumes global relationships of relative accuracy between reference data layers, ignoring local variations that could be exploited to return more precise geocodes. We propose a formalization of the selection criteria and present three alternative strategies which we term the uncertainty‐, gravitationally‐, and topologically‐based strategies. The performance of each method is evaluated against two ground truth datasets of nationwide GPS points to determine any resulting spatial improvements. We find that any of the three new methods improves on current practice in the majority of cases. The gravitationally‐ and topologically‐based approaches offer improvement over a simple uncertainty‐based approach in cases with specific characteristics. 相似文献
Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets. 相似文献