Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

Liao, Jianshang; Wang, Liguo

doi:10.1109/access.2020.2993864

Cited by 6 publications

(1 citation statement)

References 55 publications

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“…In this subsection, we report the classification experimental results for multiple datasets. We contrasted the test results not only with the aforementioned methods but also with the adaptive manifold filter and spatial correlation feature (AMSCF) [38]. AMSCF is a method for hyperspectral image classification, but it can also process ordinary images.…”

Section: E Classification Experimentsmentioning

confidence: 99%

Maximum Margin Correlation Filter Based on Data Spatial Distribution Information

et al. 2022

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In the past decade, object localization and object classification using correlation filters, especially large margin correlation filters which combine with support vector machine (SVM), have become a hotspot. However, the large margin correlation filters do not consider the class distribution and the structural features within the class during training, which is easily affected by noise. This paper presents two methods to overcome this drawback: minimum class variance large margin correlation filter (MCVLMCF) and minimum class locality preserving variance correlation filter (MCLPVCF). First, the overall structure information of the target is obtained by the within-class scatter with MCVLMCF, and the spatial features of the sample are extracted by the intrinsic manifold structure of data with MCLPVCF. Then, we embed these two types of information into the optimal function of the large margin correlation filter, fuse the sample spatial features with the large margin principle and correlation filtering, and convert it to solve the filter in the frequency domain. Finally, object localization experiments in actual environments and classification experiments on different datasets demonstrate that our proposed methods can adapt to complex object changes and achieve better performances than some state-of-the-art methods. INDEX TERMSCorrelation Filter, support vector machine, within-class scatter, intrinsic manifold structure I. INTRODUCTION Research on computer vision algorithms has been the focus of much activity in computer science. It covers a variety of application areas and academic disciplines, including target classification, target detection, digital image processing, geometric modeling, physics, and mathematics. Among these, target classification and target detection are significant computer vision research and application areas. They are frequently utilized in our lives for vehicle detection [1], pedestrian detection [2], object recognition [3], and face recognition [4]. In the past years, correlation filters have been investigated for target classification, detection, and tracking [5] because of their noise immunity, shift-invariance, and fast training. Correlation filters achieved good performance in many pattern recognition applications including face localization, pedestrian localization, and object tracking [5]. Many computer vision methods have been combined with correlation filters. Vander et al. [6] propose matched filter (MF) which is an earlier correlation filter algorithm. The construction method of the filter is the conjugate transpose after the two-dimensional Fourier transform of the training image. The structure of the MF is too simple, only the correlation output of training images is relatively accurate, and it is not practical enough, but the application of the correlation filter in image processing also provides new ideas for subsequent researchers. After a long period of improvement, the current correlation filter can be divided into two kinds: the synthetic discriminant function filter and ...

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Section: E Classification Experimentsmentioning

confidence: 99%

Maximum Margin Correlation Filter Based on Data Spatial Distribution Information

et al. 2022

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Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Liao,

Wang,

Zhao

2023

Canadian Journal of Remote Sensing

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Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

et al. 2020

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Detection of surface material based on hyperspectral imaging (HSI) analysis is an important and challenging task in remote sensing. It is widely known that spectral-spatial data exploitation performs better than traditional spectral pixel-wise procedures. Nowadays, convolutional neural networks (CNNs) have shown to be a powerful deep learning (DL) technique due their strong feature extraction ability. CNNs not only combine spectral-spatial information in a natural way, but have also shown to be able to learn translation-equivariant representations, i.e. a translation of input features into an equivalent internal CNN feature map. This provides great robustness to spatial feature locations. However, as far as we know, CNNs do not exhibit a natural way to exploit rotation equivariance, i.e. make use of the fact that data patches in a HSI data cube are observed in different orientations due to their orientation or on the varying paths/orbits of the airborne/spaceborne spectrometers. This paper presents a rotation-equivariant CNN2D model for HSI analysis, where traditional convolution kernels have been replaced by circular harmonic filters (CHFs). The obtained results over three well-known HSI datasets showcase the potential of the approach. INDEX TERMS Hyperspectral imaging (HSI), convolutional neural network (CNN), harmonic network (H-net), rotation invariance

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Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

Cited by 6 publications

References 55 publications

Maximum Margin Correlation Filter Based on Data Spatial Distribution Information

Maximum Margin Correlation Filter Based on Data Spatial Distribution Information

Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

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