Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

Patent ·
OSTI ID:1415441
;
An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC52-06NA25396
Assignee:
Los Alamos National Security, LLC (Los Alamos, NM)
Patent Number(s):
9,858,502
Application Number:
15/134,437
OSTI ID:
1415441
Country of Publication:
United States
Language:
English

References (25)

Method of optimal directions for frame design conference January 1999
Atomic Decomposition by Basis Pursuit journal January 1998
On Shift-Invariant Sparse Coding book January 2004
Supervised translation-invariant sparse coding conference June 2010
From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images journal February 2009
Unsupervised learning of sparse and shift-invariant decompositions of polyphonic music conference January 2004
A Sherman–Morrison approach to the solution of linear systems journal May 2006
Sparse and shift-Invariant representations of music journal January 2006
Dictionary Learning with Large Step Gradient Descent for Sparse Representations book January 2012
General design algorithm for sparse frame expansions journal January 2006
Image compression using learned dictionaries by RLS-DLA and compared with K-SVD conference May 2011
Task-Driven Dictionary Learning journal April 2012
Adaptive deconvolutional networks for mid and high level feature learning conference November 2011
Explicit Shift-Invariant Dictionary Learning journal January 2014
Fast Image Recovery Using Variable Splitting and Constrained Optimization journal September 2010
An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems journal March 2011
Multi-Scale Dictionary Learning Using Wavelets journal September 2011
Non-local sparse models for image restoration conference September 2009
Alternating Direction Method with Self-Adaptive Penalty Parameters for Monotone Variational Inequalities journal August 2000
Updating the Inverse of a Matrix journal June 1989
Fast Convolutional Sparse Coding conference June 2013
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers book January 2011
Deconvolutional networks conference June 2010
Learning mid-level features for recognition conference June 2010
A fast proximal method for convolutional sparse coding
  • Chalasani, Rakesh; Principe, Jose C.; Ramakrishnan, Naveen
  • 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas), The 2013 International Joint Conference on Neural Networks (IJCNN) https://doi.org/10.1109/IJCNN.2013.6706854
conference August 2013

Similar Records

Related Subjects

47 OTHER INSTRUMENTATION
97 MATHEMATICS AND COMPUTING