Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification

• URL: http://arxiv.org/abs/2402.10026v1
• Date: Thu, 15 Feb 2024 15:46:13 GMT
• Title: Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification
• Authors: Alok Ranjan Sahoo and Pavan Chakraborty
• Abstract summary: 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.
• Score: 1.2691047660244332
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hyper spectral images have drawn the attention of the researchers for its complexity to classify. It has nonlinear relation between the materials and the spectral information provided by the HSI image. Deep learning methods have shown superiority in learning this nonlinearity in comparison to traditional machine learning methods. Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features. However, it uses comparatively large number of parameters. Moreover, it is not effective to learn inter layer information. Hence, this paper proposes a neural network combining 3-D CNN, 2-D CNN and Bi-LSTM. The performance of this model has been tested on Indian Pines(IP) University of Pavia(PU) and Salinas Scene(SA) data sets. The results are compared with the state of-the-art deep learning-based models. This model performed better in all three datasets. 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.

Related papers

• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding [110.07170245531464]
  Current 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. Recent advances have shown that similar problems can be significantly alleviated by employing knowledge from other modalities, such as language. We learn a unified representation of images, texts, and 3D point clouds by pre-training with object triplets from the three modalities.
  arXiv  Detail & Related papers  (2022-12-10T01:34:47Z)
• Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
  We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition. Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
  arXiv  Detail & Related papers  (2022-10-26T15:21:39Z)
• Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning [0.0]
  We implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.
  arXiv  Detail & Related papers  (2022-07-23T12:24:52Z)
• Large-Margin Representation Learning for Texture Classification [67.94823375350433]
  This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The experimental results on texture and histopathologic image datasets have shown that the proposed approach achieves competitive accuracy with lower computational cost and faster convergence when compared to equivalent CNNs.
  arXiv  Detail & Related papers  (2022-06-17T04:07:45Z)
• Classification of Hyperspectral Images by Using Spectral Data and Fully Connected Neural Network [0.0]
  classification success over 90% has been achieved for hyperspectral images. In this study, hyperspectral images of Indian pines, Salinas, Pavia centre, Pavia university and Botswana are classified. An average accuracy of 97.5% is achieved for the test sets of all hyperspectral images.
  arXiv  Detail & Related papers  (2022-01-08T12:45:48Z)
• 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)
• Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning [2.1446056201053185]
  We propose an extensive benchmark of recent state-of-the-art (SOTA) 3D CNN, evaluating also the benefits of data augmentation and deep ensemble learning. Experiments were conducted on a large multi-site 3D brain anatomical MRI data-set comprising N=10k scans on 3 challenging tasks: age prediction, sex classification, and schizophrenia diagnosis. We found that all models provide significantly better predictions with VBM images than quasi-raw data. DenseNet and tiny-DenseNet, a lighter version that we proposed, provide a good compromise in terms of performance in all data regime
  arXiv  Detail & Related papers  (2021-06-02T13:00:35Z)
• Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN [1.2875323263074796]
  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.
  arXiv  Detail & Related papers  (2021-01-25T18:43:57Z)
• 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)

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.