Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter

Guo, Yanhui; Cao, Han; Han, Shihui; Sun, Yunchuan; Bai, Yu

doi:10.1109/access.2018.2820043

Cited by 54 publications

(21 citation statements)

References 29 publications

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“…Many other image filtering methods have been successfully applied for spectral-spatial HSI processing, for instance Kang et al [54] introduced edge-preserving filtering (EPF) to refine the SVM classification map. Guo et al [55] applied guided filtering (GF) to the KNN classifier, while Wang et al [56] combined GF, principle component analysis (PCA) and deep neural networks to extract discriminative multi-features from HSI scenes. In [57], Liao and Wang fused two spatial-based filters, in particular curvature (CF) and domain transform recursive (DTRF) filtering to enhance the performance of HSI classification, and in [58] they implemented an adaptive manifold filter with spatial correlation feature (AMSCF) approach.…”

Section: A Traditional Machine Learning Methods For Spectral-spatialmentioning

confidence: 99%

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 This paper presents a rotation-equivariant CNN2D model for HSI analysis, where traditional convolution kernels have been replaced by circular harmonic filters (CHFs).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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Section: A Traditional Machine Learning Methods For Spectral-spatialmentioning

confidence: 99%

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

et al. 2020

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“…Then, a stacked autoencoder was used to classify each pixel. Guo [24] proposed a method, which combines a guided filter, joint representation, and k-nearest neighbor to improve HSI classification. Inspired by the methods mentioned above, we propose a novel method for fusing spectral and spatial information.…”

Section: Guided Filter and Hsi Classificationmentioning

confidence: 99%

Hyperspectral image classification with SVM and guided filter

Guo

Yin

Zhao

et al. 2019

J Wireless Com Network

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Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement.

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“…Briefly, as d increases, a d goes to zero, which stops the propagation chain, indicating that the neighborhood pixels are in the same ground. Equation (14) is an asymmetric causal filter and depended on input and output information. To obtain the filtering symmetry, this equation needs to be executed twice, such as the procedures: first from left to right, and then from right to left; or from top to bottom, and then from bottom to top [25].…”

Section: Domain Transform Recursive Filter (Dtrf)mentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Liao

Wang

2019

Remote Sensing

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In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.

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Spectral–Spatial HyperspectralImage Classification With K-Nearest Neighbor and Guided Filter

Cited by 54 publications

References 29 publications

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

Hyperspectral image classification with SVM and guided filter

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

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