Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification

• URL: http://arxiv.org/abs/2006.12556v1
• Date: Thu, 18 Jun 2020 10:51:04 GMT
• Title: Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification
• Authors: G.Kalaiarasi, S.Maheswari
• Abstract summary: A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image. The FFSIFT-MLPC technique performs three major processes, namely preprocessing, feature extraction and classification using multiple layers. The results evident that presented FFSIFT-MLPC technique improves the hyperspectral image classification accuracy, PSNR and minimizes false positive rate as well as classification time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperspectral image (HSI) classification plays a significant in the field of remote sensing due to its ability to provide spatial and spectral information. Due to the rapid development and increasing of hyperspectral remote sensing technology, many methods have been developed for HSI classification but still a lack of achieving the better performance. A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image with higher accuracy and minimum time consumption. The FFSIFT-MLPC technique performs three major processes, namely preprocessing, feature extraction and classification using multiple layers. Initially, the hyperspectral image is divided into number of spectral bands. These bands are given as input in the input layer of perceptron. Then the Frost filter is used in FFSIFT-MLPC technique for preprocessing the input bands which helps to remove the noise from hyper-spectral image at the first hidden layer. After preprocessing task, texture, color and object features of hyper-spectral image are extracted at second hidden layer using Gaussian distributive scale-invariant feature transform. At the third hidden layer, Euclidean distance is measured between the extracted features and testing features. Finally, feature matching is carried out at the output layer for hyper-spectral image classification. The classified outputs are resulted in terms of spectral bands (i.e., different colors). Experimental analysis is performed with PSNR, classification accuracy, false positive rate and classification time with number of spectral bands. The results evident that presented FFSIFT-MLPC technique improves the hyperspectral image classification accuracy, PSNR and minimizes false positive rate as well as classification time than the state-of-the-art methods.

Related papers

• Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
  We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network. Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
  arXiv  Detail & Related papers  (2024-08-28T10:07:17Z)
• DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification [6.521187080027966]
  We propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification.
  arXiv  Detail & Related papers  (2023-10-29T15:26:37Z)
• Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure [1.491109220586182]
  This paper presents an innovative spectral unmixing approach for hyperspectral images (HSIs) based on block-sparse structure and sparse Bayesian learning strategy.
  arXiv  Detail & Related papers  (2022-04-10T09:37:41Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution [82.59267937569213]
  High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution. We present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples.
  arXiv  Detail & Related papers  (2021-08-31T02:13:00Z)
• SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers [91.09957836250209]
  Hyperspectral (HS) images are characterized by approximately contiguous spectral information. CNNs have been proven to be a powerful feature extractor in HS image classification. We propose a novel backbone network called ulSpectralFormer for HS image classification.
  arXiv  Detail & Related papers  (2021-07-07T02:59:21Z)
• Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain [88.7339322596758]
  We present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery. SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.
  arXiv  Detail & Related papers  (2021-03-02T16:45:08Z)
• LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data [6.230751621285322]
  Hyperspectral (HS) and multispectral (MS) image fusion aims at estimating a high-resolution spectral image from a low-spatial-resolution HS image and a low-spectral-resolution MS image. In this work, a deep learning architecture under the algorithm unrolling approach is proposed for solving the fusion problem from HS and MS compressive measurements.
  arXiv  Detail & Related papers  (2021-03-01T12:04:42Z)
• Fusion of Dual Spatial Information for Hyperspectral Image Classification [26.304992631350114]
  A novel hyperspectral image classification framework using the fusion of dual spatial information is proposed. Experiments performed on three data sets from different scenes illustrate that the proposed method can outperform other state-of-the-art classification techniques.
  arXiv  Detail & Related papers  (2020-10-23T12:20:18Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)
• Two-dimensional Multi-fiber Spectrum Image Correction Based on Machine Learning Techniques [8.754036933225398]
  We propose a novel method to solve the problem of spatial variation PSF through image aberration correction. When CCD image aberration is corrected, PSF, the convolution kernel, can be approximated by one spatial invariant PSF only.
  arXiv  Detail & Related papers  (2020-02-16T15:39:09Z)

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.