Hyperspectral image classification based on local binary pattern and broad learning system

Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System

“…is the result of BLS classification, where C is the quantity of sample types. There are d feature mappings, and each mapping has e nodes, can be represented as in Equation 3 [19]  …”

“…represents all mapped feature nodes. In order to capture the sparse and compact features, we make use of the sparse auto encoder to fine-tune the initial W fe i [19]. Then, Equation 4is utilized to compute enhancement nodes from mapped feature nodes…”

“…is the concatenation of all the first l groups of enhancement nodes [19]. Combined with Equation (4), the output result of BLS can be expressed by Equation (5) (5) where W op is the connecting weight matrix from all mapped feature nodes and all enhancement nodes to the output of the BLS.…”

“…These feature extraction methods (PCA, ICA, and LDA) cannot describe the complex spectral features structure of HSIs. LPP, which is essentially a linear model of Laplacian feature mapping, can describe the nonlinear manifold structure of data and is widely used in the spectral feature extraction of HSIs [18,19]. He et al [20] applied multiscale super-pixel-wise LPP to HSI classification.…”

“…Samat et al [37] used the extra-trees and maximally stable extremal regionguided morphological profile (MSER_MP) to achieve the ideal classification effect. The broad learning system (BLS) classification architecture based on LPP and local binary pattern (LBP)(LPP_LBP_BLS) [19] was proposed to gain the high-precision classification. However, LBP only uses the local features of pixels and needs to use an adjacency matrix, which requires a lot of calculation.…”