52,003 research outputs found
A Neural Network Method for Mixture Estimation for Vegetation Mapping
While most forest maps identify only the dominant vegetation class in delineated stands, individual stands are often better characterized by a mix of vegetation types. Many land management applications, including wildlife habitat studies, can benefit from knowledge of mixes. This paper examines various algorithms that use data from the Landsat Thematic Mapper (TM) satellite to estimate mixtures of vegetation types within forest stands. Included in the study are maximum likelihood classification and linear mixture models as well as a new methodology based on the ARTMAP neural network. Two paradigms are considered: classification methods, which describe stand-level vegetation mixtures as mosaics of pixels, each identified with its primary vegetation class; and mixture methods, which treat samples as blends of vegetation, even at the pixel level. Comparative analysis of these mixture estimation methods, tested on data from the Plumas National Forest, yields the following conclusions: (1) accurate estimates of proportions of hardwood and conifer cover within stands can be obtained, particularly when brush is not present in the understory; (2) ARTMAP outperforms statistical methods and linear mixture models in both the classification and the mixture paradigms; (3) topographic correction fails to improve mapping accuracy; and (4) the new ARTMAP mixture system produces the most accurate overall results. The Plumas data set has been made available to other researchers for further development of new mapping methods and comparison with the quantitative studies presented here, which establish initial benchmark standards.National Science Foundation (IRI 94-0165, SBR 95-13889); Office of Naval Research (N00014-95-1-0409, N00014-95-0657); Region 5 Remote Sensing Laboratory of the U.S. Forest Servic
Multitask Diffusion Adaptation over Networks
Adaptive networks are suitable for decentralized inference tasks, e.g., to
monitor complex natural phenomena. Recent research works have intensively
studied distributed optimization problems in the case where the nodes have to
estimate a single optimum parameter vector collaboratively. However, there are
many important applications that are multitask-oriented in the sense that there
are multiple optimum parameter vectors to be inferred simultaneously, in a
collaborative manner, over the area covered by the network. In this paper, we
employ diffusion strategies to develop distributed algorithms that address
multitask problems by minimizing an appropriate mean-square error criterion
with -regularization. The stability and convergence of the algorithm in
the mean and in the mean-square sense is analyzed. Simulations are conducted to
verify the theoretical findings, and to illustrate how the distributed strategy
can be used in several useful applications related to spectral sensing, target
localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio
Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)
We propose a new approach within the versatile framework of convex
optimization to solve the radio-interferometric wideband imaging problem. Our
approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and l21
minimization problems promoting low rankness and joint average sparsity of the
wideband model cube. On the one hand, enforcing low rankness enhances the
overall resolution of the reconstructed model cube by exploiting the
correlation between the different channels. On the other hand, promoting joint
average sparsity improves the overall sensitivity by rejecting artefacts
present on the different channels. An adaptive Preconditioned Primal-Dual
algorithm is adopted to solve the minimization problem. The algorithmic
structure is highly scalable to large data sets and allows for imaging in the
presence of unknown noise levels and calibration errors. We showcase the
superior performance of the proposed approach, reflected in high-resolution
images on simulations and real VLA observations with respect to single channel
imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software.
Our MATLAB code is available online on GITHUB
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