Abstract
Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.
Highlights
Hyperspectral images (HSIs) with high spectral resolution and fine spatial resolution are accessible on account of advanced sensor technology, which have been intensively studied and widely applied in many fields, such as environmental monitoring [1], precision agriculture [2], urban planning [3], and Earth observation [4]
This means that the neighbors obtained by SLS distance (SLSD) mostly belong to the same class as the target sample, which indicates that SLSD is excellent for correctly determining the pixel relationship in HSIs
In order to verify the superiority of two uSLSML methods, seven state-of-art dimensionality reduction (DR) algorithms were selected for comparison, including neighborhood preserving embedding (NPE) [21], locality preserving projection (LPP) [20], regularized local discriminant embedding (RLDE) [14], LPNPE [14], spatial and spectral Regularized local discriminant embedding (RLDE) (SSRLDE) [14], SSMRPE [24], and spatial-spectral local discriminant projection (SSLDP) [27]
Summary
Hyperspectral images (HSIs) with high spectral resolution and fine spatial resolution are accessible on account of advanced sensor technology, which have been intensively studied and widely applied in many fields, such as environmental monitoring [1], precision agriculture [2], urban planning [3], and Earth observation [4]. The methods mentioned above either neglect the location coordinates or the local spatial neighborhood characteristics and lack a comprehensive exploration of spectral-locational-spatial (SLS) information To address this issue, two unsupervised SLS manifold learning (uSLSML) methods were proposed for uDR of HSIs, called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE). Conventional reconstruction weights are calculated only based on spectral information, which cannot truly reflect the relationship among samples because there is inevitable noise and high dimensionality in HSIs and even different objects may have similar spectral properties To address this issue, SLSRPE redefines new reconstruction weights based on wSL data, which does consider the spectral-locational information and the local spatial neighborhood, which allows SLS information to be integrated into the projection for achieving more efficient manifold reconstruction.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
