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Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network.

Chenming Li1, Simon X Yang2, Yao Yang3

  • 1College of Computer and Information, Hohai University, Nanjing 211100, China. lichenming55@163.com.

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|October 27, 2018
PubMed
Summary

This study introduces an improved convolutional neural network with overlapping pooling to enhance hyperspectral image classification. The new method effectively preserves image details and boosts classification accuracy compared to traditional methods.

Keywords:
convolution neural networkhyperspectral remote sensing imageimage classificationremote sensors

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Area of Science:

  • Computer Science
  • Remote Sensing
  • Image Processing

Background:

  • Traditional convolutional neural networks often use non-overlapping average pooling, which can lead to feature similarity and loss of detail in hyperspectral images.
  • This loss of detail hinders accurate image classification, particularly for continuous spectrum hyperspectral data.

Purpose of the Study:

  • To propose and evaluate a novel overlapping pooling method for convolutional neural networks to improve hyperspectral image classification.
  • To address the limitations of average pooling in extracting distinct and detailed features from hyperspectral imagery.

Main Methods:

  • An improved convolutional neural network architecture was developed, incorporating an overlapping pooling strategy.
  • Maximum pooling was utilized with a smaller step size than the pooling kernel size to ensure overlap and coverage.
  • The Indian Pines dataset, acquired by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor, was used for experimental validation.

Main Results:

  • The proposed overlapping pooling method effectively improved the extraction of image details in hyperspectral remote sensing data.
  • Experimental results demonstrated a significant enhancement in classification accuracy when using the improved convolutional neural network.

Conclusions:

  • The novel overlapping pooling method offers a superior approach for hyperspectral image classification compared to traditional non-overlapping pooling.
  • This technique enhances feature extraction, leading to higher accuracy and better preservation of image details in remote sensing applications.