The influence of multispectral scanner spatial resolution on forest feature classification

Inappropriate spatial resolution and corresponding data processing techniques may be major causes for non-optimal forest classification results frequently achieved from multispectral scanner (MSS) data. Procedures and results of empirical investigations are studied to determine the influence of MSS spatial resolution on the classification of forest features into levels of detail or hierarchies of information that might be appropriate for nationwide forest surveys and detailed in-place inventories. Two somewhat different, but related studies are presented. The first consisted of establishing classification accuracies for several hierarchies of features as spatial resolution was progressively coarsened from (2 meters) squared to (64 meters) squared. The second investigated the capabilities for specialized processing techniques to improve upon the results of conventional processing procedures for both coarse and fine resolution data

STNet: Selective Tuning of Convolutional Networks for Object Localization

Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feedforward processing on the abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of Convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps

Towards Conceptual Multidimensional Design in Decision Support Systems

International audienceMultidimensional databases support efficiently on-line analytical processing (OLAP). In this paper, we depict a model dedicated to multidimensional databases. The approach we present designs decisional information through a constellation of facts and dimensions. Each dimension is possibly shared between several facts and it is organised according to multiple hierarchies. In addition, we define a comprehensive query algebra regrouping the more popular multidimensional operations in current commercial systems and research approaches. We introduce new operators dedicated to a constellation. Finally, we describe a prototype that allows managers to query constellations of facts, dimensions and multiple hierarchies

Semantic modeling usuing theta-roles: a natural language processing-based approach to conceptual representation

This paper presents a natural language processing semantic modeling approach that can be automated to pull conceptual information from text documents. The approach is based in the use of theta-roles, in particular thematic-hierarchies, to create a collection of vectors culled from the sentences of a document that describe content of the document. This approach can form the basis of a natural language processing-based concept representation scheme

Economics of Information Processing in Operations Organizations

This paper studies a fundamental management question: how does information economics affect the organization of management? We view management hierarchies as tree-like structures designed to minimize real and opportunity costs related to information processing and decision making. “Line” production activities stand at the end nodes of a hierarchy tree. Data from these bottom nodes are processed and distributed to higher level nodes that combine information from the lower nodes. The question we ask is: “how do the real and opportunity costs of information processing affect the tree”. We solve for the optimal tree which includes the links and capacity at each of the nodes. Models are formulated on two underlying premises: complexity costs arise due to processing different types of data, and queuing effects due to data arrival and processing uncertainties create delay which is an opportunity cost