Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Eryang Chen,Fang Miao,Kaibo Shi,Ruichun Chang,Youhua Wei,Ansheng Ye,Jianghong Yuan,Ke Guo,Yiping Li,Zi-Peng Wang

doi:10.1155/2021/9998185

Abstract

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

Highlights

Due to the advancements in remote sensing technology, hyperspectral images (HSIs) are containing increasing spectral and spatial information (SSI), resulting in their extensive use in domains, such as forest inventory [1], urban area monitoring [2], road extraction [3], geological surveys [4], precision agriculture [5], environmental protection [6], military applications [7], hydrocarbon detection [8], oil reservoir exploration [9], and lake sediment analysis [10]
Because of their adaptive properties, the optimal parameters can be determined without artificial auxiliaries. e framework comprises three main modules: (1) Preprocessing the HSI image by applying maximum noise fraction (MNF), the noise in the HSI can be removed effectively during dimension reduction. is result avoids the problem of the dimension
Experimental Datasets. ree hyperspectral datasets were employed to evaluate the performance of the SS-MNFARMG

Summary

Introduction

Due to the advancements in remote sensing technology, hyperspectral images (HSIs) are containing increasing spectral and spatial information (SSI), resulting in their extensive use in domains, such as forest inventory [1], urban area monitoring [2], road extraction [3], geological surveys [4], precision agriculture [5], environmental protection [6], military applications [7], hydrocarbon detection [8], oil reservoir exploration [9], and lake sediment analysis [10]. HSI classification is a crucial research topic related to these applications. In contrast to those of Synthetic Aperture Radar (SAR) [11] or RGB images [12], the two main challenges associated with HSI classification are the high dimensionality of the dataset and the redundancy of spectral information. Many approaches for HSI dimension reduction have been proposed. Uddin et al [13] proposed an information-theoretic normalized-based PCA for HSI classification. Fu et al [14] proposed a segmented PCA-based algorithm for HSI classification

Methods

Results

Conclusion