Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN

• URL: http://arxiv.org/abs/2101.10532v1
• Date: Mon, 25 Jan 2021 18:43:57 GMT
• Title: Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
• Authors: Muhammad Ahmad, Sidrah Shabbir, Rana Aamir Raza, Manuel Mazzara, Salvatore Distefano, Adil Mehmood Khan
• Abstract summary: 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. This work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost.
• Score: 1.2875323263074796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images. However, 2D CNN only considers the spatial information and ignores the spectral information whereas 3D CNN jointly exploits spatial-spectral information at a high computational cost. Therefore, this work proposed a lightweight CNN (3D followed by 2D-CNN) model which significantly reduces the computational cost by distributing spatial-spectral feature extraction across a lighter model alongside a preprocessing that has been carried out to improve the classification results. Five benchmark Hyperspectral datasets (i.e., SalinasA, Salinas, Indian Pines, Pavia University, Pavia Center, and Botswana) are used for experimental evaluation. The experimental results show that the proposed pipeline outperformed in terms of generalization performance, statistical significance, and computational complexity, as compared to the state-of-the-art 2D/3D CNN models except commonly used computationally expensive design choices.

Related papers

• Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification [1.2691047660244332]
  This paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. It could achieve 99.83, 99.98 and 100 percent accuracy using only 30 percent trainable parameters of the state-of-art model in IP, PU and SA datasets respectively.
  arXiv  Detail & Related papers  (2024-02-15T15:46:13Z)
• Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks [18.644268589334217]
  This letter designed the Faster selective kernel mechanism network (FSKNet), FSKNet can balance this problem. It designs 3D-CNN and 2D-CNN conversion modules, using 3D-CNN to complete feature extraction while reducing the dimensionality of spatial and spectrum. FSKNet achieves high accuracy on the IN, UP, Salinas, and Botswana data sets with very small parameters.
  arXiv  Detail & Related papers  (2022-02-14T02:14:50Z)
• Classification of diffraction patterns using a convolutional neural network in single particle imaging experiments performed at X-ray free-electron lasers [53.65540150901678]
  Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is particularly well suited to determine the 3D structure of particles in their native environment. For a successful reconstruction, diffraction patterns originating from a single hit must be isolated from a large number of acquired patterns. We propose to formulate this task as an image classification problem and solve it using convolutional neural network (CNN) architectures.
  arXiv  Detail & Related papers  (2021-12-16T17:03:14Z)
• OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression [59.58879331876508]
  We study the learning of representation models for omnidirectional images and propose to use the properties of HEALPix uniform sampling of the sphere to redefine the mathematical tools used in deep learning models for omnidirectional images. Our proposed on-the-sphere solution leads to a better compression gain that can save 13.7% of the bit rate compared to similar learned models applied to equirectangular images.
  arXiv  Detail & Related papers  (2021-07-19T22:14:30Z)
• Continual 3D Convolutional Neural Networks for Real-time Processing of Videos [93.73198973454944]
  We introduce Continual 3D Contemporalal Neural Networks (Co3D CNNs) Co3D CNNs process videos frame-by-frame rather than by clip by clip. We show that Co3D CNNs initialised on the weights from preexisting state-of-the-art video recognition models reduce floating point operations for frame-wise computations by 10.0-12.4x while improving accuracy on Kinetics-400 by 2.3-3.8x.
  arXiv  Detail & Related papers  (2021-05-31T18:30:52Z)
• SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification [0.0]
  Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. We propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification.
  arXiv  Detail & Related papers  (2021-04-01T08:45:15Z)
• Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction [57.74609918453932]
  Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses. Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images. We argue that preserving the continuity of extracted vessels requires to take into account the global geometry. We propose a hybrid representation learning approach to address this challenge.
  arXiv  Detail & Related papers  (2020-12-14T05:22:49Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)
• Hyperspectral Image Classification with Attention Aided CNNs [33.82700423556775]
  We propose an attention aided CNN model for spectral-spatial classification of hyperspectral images. The proposed model can achieve superior performance compared to several state-of-the-art CNN-related models.
  arXiv  Detail & Related papers  (2020-05-25T08:40:56Z)
• A Fast 3D CNN for Hyperspectral Image Classification [0.456877715768796]
  Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial information. This work proposed a 3D CNN model that utilizes both spatial-spectral feature maps to attain good performance.
  arXiv  Detail & Related papers  (2020-04-29T12:57:36Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.