Abstract
In this paper, we propose a new method for hyperspectral images (HSI) classification, aiming to take advantage of both manifold learning-based feature extraction and neural networks by stacking layers applying locality sensitive discriminant analysis (LSDA) to broad learning system (BLS). BLS has been proven to be a successful model for various machine learning tasks due to its high feature representative capacity introduced by numerous randomly mapped features. However, it also produces redundancy, which is indiscriminate and finally lowers its performance and causes heavy computing demand, especially in cases of the input data bearing high dimensionality. In our work, a manifold learning method is integrated into the BLS by inserting two LSDA layers before the input layer and output layer separate, so the spectral-spatial HSI features are fully utilized to acquire the state-of-the-art classification accuracy. The extensive experiments have shown our method’s superiority.
Highlights
Hyperspectral images (HSIs) are produced by hyperspectral sensors by capturing reflectance values on tens or even hundreds of spectral bands for each pixel. e increased spectral resolution of hyperspectral images (HSI) makes them essential for many remote sensing tasks in various fields, such as agriculture [1], environment [2], and military [3], etc
We present our experimental results on each dataset in Tables (2), (3), and (4)
Based on the results shown in all tables, we can find that our proposed broad learning system (BLS)-Locality sensitive discriminant analysis (LSDA) is superior to the classic methods (i.e., support vector machine (SVM) and KELM) and their derivations with spectral-spatial kernel method (i.e., SVM with CK (SVM-CK) and KELM-CK), as well as recent prominent methods (i.e., HiFi-We and MASR) focusing on exploring the advantage of spectral-spatial filters in HSI classification
Summary
Hyperspectral images (HSIs) are produced by hyperspectral sensors by capturing reflectance values on tens or even hundreds of spectral bands for each pixel. e increased spectral resolution of HSIs makes them essential for many remote sensing tasks in various fields, such as agriculture [1], environment [2], and military [3], etc. To obtain semantic abstraction from HSIs, classification requires mapping from pixel values to land-use and/or land-cover descriptions, which is nontrivial because the high spectral redundancy detrimentally affects the classification process in terms of the curse of dimensionality problem [4] and noisy labels [5]. Accompanied by increasing spatial resolution, the widespread adoption of integrated spatial and spectral information in HSIs’ analysis has further increased the dimensionality of input data [6]. It has been proven in many cases that the useful spectral information for HSIs classification implies a nonlinear embedded submanifold of the original feature space, which can be retrieved by manifoldlearning-based feature extraction methods [7, 8]. Unlike other NNG based approaches (e.g., locality preserving projections (LPP) [18] and LE) [12], LSDA was used within-class graph and between-class graph to obtain good between-class separation and preserve the within-class
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