Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

Paoletti, Mercedes E.; Haut, Juan M.; Roy, Swalpa Kumar; Hendrix, E.M.T.

doi:10.1109/access.2020.3027776

Cited by 28 publications

(21 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The discriminative power of the extracted features can be further improved by combining both the max and min convolutional features before the ReLU non-linearity reported in [191] for the classification task. CNN's are failed to exploit rotation equivariance in a natural way [192] introduced the translation equivariant representations of input features which provides extra robustness to the spatial feature locations for HSIC.…”

Section: Spectral-spatial Cnn Framework For Hsicmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

190

View full text Add to dashboard Cite

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the stateof-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that

show abstract

Section: Spectral-spatial Cnn Framework For Hsicmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

190

View full text Add to dashboard Cite

show abstract

“…the approximation is tight. Totally analogous to (18) and (19), we can also modify this approximation to make it more smooth: (20). For the old approximation we chose ν = 44, as suggested in [48], and for the new one ν = 1.6.…”

Section: Sub-riemannian Approximationmentioning

confidence: 99%

“…w 1 = w 3 = 1 and w 2 = 8. We see the exact distance d alongside the old sub-Riemannian approximation ρ b,sr,old(19) and new approximation ρ b,sr…”

mentioning

confidence: 99%

Analysis of (sub-)Riemannian PDE-G-CNNs

Bellaard

Bon

Pai

et al. 2022

Preprint

View full text Add to dashboard Cite

Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been hard-coded in the network. The recently introduced framework of PDE-based G-CNNs (PDE-G-CNNs) generalises G-CNNs. PDE-G-CNNs have the core advantages that they simultaneously 1) reduce network complexity, 2) increase classification performance, and 3) provide geometric interpretability. Their implementations primarily consist of linear and morphological convolutions with kernels. In this paper we show that the previously suggested approximative morphological kernels do not always accurately approximate the exact kernels accurately. More specifically, depending on the spatial anisotropy of the Riemannian metric, we argue that one must resort to sub-Riemannian approximations. We solve this problem by providing a new approximative kernel that works regardless of the anisotropy. We provide new theorems with better error estimates of the approximative kernels, and prove that they all carry the same reflectional symmetries as the exact ones. We test the effectiveness of multiple approximative kernels within the PDE-G-CNN framework on two datasets, and observe an improvement with the new approximative kernels. We report that the PDE-G-CNNs again allow for a considerable reduction of network complexity while having comparable or better performance than G-CNNs and CNNs on the two datasets. Moreover, PDE-G-CNNs have the advantage of better geometric interpretability over G-CNNs, as the morphological kernels are related to association fields from neurogeometry.

show abstract

“…As stated in [ 195 , 198 ], PCA is exploited at its best for feature extraction, selection, and reduction to achieve higher accuracy and performance quality. PCA is one of the best preprocessing methods considered to date for improvised spectral dimension reduction [ 180 ], proper selection of spectral bands and their multiscale features in a segmented format [ 181 , 199 ], noise-reduced spectral analysis [ 27 ], and feature extraction [ 130 , 196 ]. PCA, in collaboration with SVM [ 195 , 200 ], DL for feature reduction and better classification [ 182 , 183 ], CNN with multiscale feature extraction [ 188 , 189 ], and sparse tensor technology [ 190 ], has highly been appreciated as soulful research.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Datta

Mallick

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.

show abstract

Rotation Equivariant Convolutional Neural Networks for Hyperspectral Image Classification

Cited by 28 publications

References 118 publications

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Analysis of (sub-)Riemannian PDE-G-CNNs

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

Contact Info

Product

Resources

About