Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

Abstract

Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with MSLPP–ESMA–SVR model

“…In response to the limitations existing in linear methods, manifold learning methods designed to retain local structural features have been proposed. Within this context, locality preserving projections (LPP) emerge as a nonlinear manifold learning method for feature extraction, preserving local feature information by maintaining the neighborly relationships among individual data samples (He et al, 2019). Pu et al (2016) applied LPP algorithm to dimensionality reduction of the algal concentration dataset, and demonstrated the potential of HSI combined with LPP feature extraction to classify water samples with different algal concentration levels.…”

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

“…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. Deng et al [21] proposed the tensor locality preserving projection (TLPP) algorithm to reduce the dimensionality of HSI.…”