Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

Abstract

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.Keywords: hyperspectral image (HSI) classification; sparse multinomial logistic regression (SMLR); extreme sparse multinomial logistic regression (ESMLR); extended multi-attribute profiles (EMAPs); linear multiple features learning (MFL); Lagrange multiplier

Local Block Multilayer Sparse Extreme Learning Machine for Effective Feature Extraction and Classification of Hyperspectral Images

“…In addition, other spatial features including local binary pattern (LBPn) [28] and attribute profile (AP) [11] are compared with the proposed LBMSELM, where the default settings of parameters are used for these two approaches. With 1% labeled samples for training and the remaining for testing, the experimental results are reported in Tables 11 and 12 for comparison.…”

“…To tackle these problems, a number of state-of-the-art algorithms have been proposed, such as the support vector machine [8] (SVM), the multi-kernel classification [9] (MK), the sparse multinomial logistic regression [10][11] and the extreme learning machine [12][13] (ELM). Besides, a number of methods have also been proposed for feature extraction, such as principal component analysis (PCA) and its variations [14][15][16], segmented auto-encoder [17] and singular spectrum analysis (SSA) [18][19][20].…”

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

“…With rich spectral information contained in tens or hundreds of spectral bands, hyperspectral images (HSI) has been successfully applied in a wide range of remote sensing applications such as land cover analysis [1][2][3], military surveillance [4,5], object detection [6], and precision agriculture [7][8][9][10][11], etc. Among these applications, image classification is an active topic, which aims to assign each pixel in the HSI into one unique semantic category or class.…”

“…Input: initialize feature weight ω k 0 = 1 K ; Metric M k 0 , k = 1, ..., K; β; η; 2: Initialisation: Generate P training sample pairs randomly: , where p = 1, ..., P and k = 1, ...K. 3: for p = 1 to P do for k = 1 to K do case if φ 1 holds true then 7:ξ = 1, update weight ω k,p by(10) and M k p by(12); update weight ω k,p using Equation(10)and M k p using Equation(12); Output: M k , k = 1, ..., K and ω k , k = 1, ...K;…”

Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

“…Many scholars around the world have successfully studied various classification methods of HSI, including sparse representation-based techniques [12], Bayesian estimation method [13], K-mean [14], maximum likelihood [15], multinomial logistic regression [16] and deep learning [17]. More specifically, Support Vector Machine (SVM) has been fruitfully applied in HSI classification and achieved respectable results [18].…”