Real-time online processing for remote sensing imagery

Qian Du; Bang-er Shia

doi:10.1117/12.485962

23 September 2003 Real-time online processing for remote sensing imagery

Qian Du, Bang-er Shia

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.485962
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

The realtime implementation is discussed for several detection and classification techniques: Orthogonal Subspace Projection (OSP), Filter Vector Algorithm (FVA), Generalized Likelihood Ratio Test (GLRT), RX algorithm, Constrained Energy Minimization (CEM), Target Constrained Interference Minimization Filter (TCIMF), and Constrained Linear Discriminant Analysis (CLDA). Two data dimensionality limitations are met in realtime processing. One is the number of classes to be classified cannot be larger than data dimensionality, i.e., the number of spectral bands (for some techniques), and the other is the number of independent pixel vectors used for processing must be larger than the number of bands for a data sample correlation or covariance matrix with full rank (for some techniques). In this paper, we present methods to take care of these two limitations: the former is solved by generating artificial band images to expand the data dimensionality, while the latter is solved by using a positive definite correlation matrix as initial matrix. Experiments using hyperspectral data and multispectral data demonstrate the effectiveness of these methods.

Citation Download Citation

Qian Du and Bang-er Shia "Real-time online processing for remote sensing imagery", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.485962

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