UVE-LLE Classification of Apple Mealiness Based on Hyperspectral Scattering Image

Bo-jin, 汪泊锦 Wang; Min, 黄敏 Huang; Qibing, 朱启兵 Zhu; Shuang, 王爽 Wang

doi:10.3788/gzxb20114008.1132

Cited by 3 publications

(1 citation statement)

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“…The improved ISOMAP algorithm selects neighborhood according to spectral Angle, avoiding neighborhood instability in high-dimensional spectral space [18]. Wang et al proposed a method combining UVE and LLE, which used LLE to reduce the dimension of the image composed of effective wavelength, and used partial least squares discriminant analysis to establish the classification model [19]. In order to solve the local tangent space alignment in the adaptability of higher order information loss problems in the manifold, such as Yang et al proposed a local neighborhood information extraction of the new algorithm optimization [20], through the optimization of the extraction of tangent vector, which can improve higher dimensional nonuniform distribution manifold dimensionality reduction effect, the proposed algorithm can effectively reconstruct density curve of low dimensional coordinates, the low dimensional high-dimensional image has good adaptability.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Ding

Shao

et al. 2021

Electronics

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Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.

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Section: Introductionmentioning

confidence: 99%