Combating climate change with matching-commitment agreements

Countries generally agree that global greenhouse gas emissions are too high, but prefer other countries reduce emissions rather than reducing their own. The Paris Agreement is intended to solve this collective action problem, but is likely insufficient. One proposed solution is a matching-commitment agreement, through which countries can change each other’s incentives by committing to conditional emissions reductions, before countries decide on their unconditional reductions. Here, we study matching-commitment agreements between two heterogeneous countries. We find that such agreements (1) incentivize both countries to make matching commitments that in turn incentivize efficient emissions reductions, (2) reduce emissions from those expected without an agreement, and (3) increase both countries’ welfare. Matching-commitment agreements are attractive because they do not require a central enforcing authority and only require countries to fulfil their promises; countries are left to choose their conditional and unconditional emissions reductions according to their own interests

Detection of compound structures by region group selection from hierarchical segmentations

Detection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE

Detection of compound structures using hierarchical clustering of statistical and structural features

We describe a new procedure that combines statistical and structural characteristics of simple primitive objects to discover compound structures in images. The statistical information that is modeled using spectral, shape, and position data of individual objects, and structural information that is modeled in terms of spatial alignments of neighboring object groups are encoded in a graph structure that contains the primitive objects at its vertices, and the edges connect the potentially related objects. Experiments using WorldView-2 data show that hierarchical clustering of these vertices can find high-level compound structures that cannot be obtained using traditional techniques. © 2011 IEEE

Detection of compound structures using multiple hierarchical segmentations

In this paper, our aim is to discover compound structures comprised of regions obtained from hierarchical segmentations of multiple spectral bands. A region adjacency graph is constructed by representing regions as vertices and connecting these vertices that are spatially close by edges. Then, dissimilarities between neighboring vertices are computed using statistical and structural features, and are assigned as edge weights. Finally, the compound structures are detected by extracting the connected components of the graph whose edges with relatively large weights are removed. Experiments using WorldView-2 images show that grouping of these vertices according to different criteria can extract high-level compound structures that cannot be obtained using traditional techniques. © 2012 IEEE

Automated detection of objects using multiple hierarchical segmentations

We introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed by combining structural information extracted by morphological processing with spectral information summarized using principal components analysis. Then, segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity are selected as candidate structures for object detection. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the object-conditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on two data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. © 2007 IEEE

Morphological segmentation of urban structures

Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information. © 2007 IEEE

Multi-resolution segmentation and shape analysis for remote sensing image classification

We present an approach for classification of remotely sensed imagery using spatial information extracted from multi-resolution approximations. The wavelet transform is used to obtain multiple representations of an image at different resolutions to capture different details inherently found in different structures. Then, pixels at each resolution are grouped into contiguous regions using clustering and mathematical morphology-based segmentation algorithms. The resulting regions are modeled using the statistical summaries of their spectral, textural and shape properties. These models are used to cluster the regions, and the cluster memberships assigned to each region in multiple resolution levels are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with two ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information. © 2005 IEEE

Building detection using directional spatial constraints

We propose an algorithm for automatic detection of buildings with complex shapes and roof structures in very high spatial resolution remotely sensed images. First, an initial oversegmentation is obtained. Then, candidate building regions are found using shadow and sun azimuth angle information. Finally, the building regions are selected by clustering the candidate regions using minimum spanning trees. The experiments on Ikonos scenes show that the algorithm is able to detect buildings with complex appearances and shapes. © 2010 IEEE

Hierarchical segmentation of complex structures

We present an unsupervised hierarchical segmentation algorithm for detection of complex heterogeneous image structures that are comprised of simpler homogeneous primitive objects. An initial segmentation step produces regions corresponding to primitive objects with uniform spectral content. Next, the transitions between neighboring regions are modeled and clustered. We assume that the clusters that are dense and large enough in this transition space can be considered as significant. Then, the neighboring regions belonging to the significant clusters are merged to obtain the next level in the hierarchy. The experiments show that the algorithm that iteratively clusters and merges region groups is able to segment high-level complex structures in a hierarchical manner. © 2010 IEEE

Effective Mass Dirac-Morse Problem with any kappa-value

The Dirac-Morse problem are investigated within the framework of an approximation to the term proportional to $1/r^2$ in the view of the position-dependent mass formalism. The energy eigenvalues and corresponding wave functions are obtained by using the parametric generalization of the Nikiforov-Uvarov method for any $\kappa$-value. It is also studied the approximate energy eigenvalues, and corresponding wave functions in the case of the constant-mass for pseudospin, and spin cases, respectively.Comment: 12 page