Comparison of CNN algorithms on hyperspectral image classification in agricultural lands
Journal
Sensors (Switzerland)
Journal Volume
20
Journal Issue
6
Date Issued
2020
Author(s)
Hsieh, T.-H.
Abstract
Several versions of convolutional neural network (CNN) were developed to classify hyperspectral images (HSIs) of agricultural lands, including 1D-CNN with pixelwise spectral data, 1D-CNN with selected bands, 1D-CNN with spectral-spatial features and 2D-CNN with principal components. The HSI data of a crop agriculture in Salinas Valley and a mixed vegetation agriculture in Indian Pines were used to compare the performance of these CNN algorithms. The highest overall accuracy on these two cases are 99.8% and 98.1%, respectively, achieved by applying 1D-CNN with augmented input vectors, which contain both spectral and spatial features embedded in the HSI data. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Subjects
Agriculture; Convolutional neural network (CNN); Hyperspectral image (HSI); Principal component analysis (PCA)
Other Subjects
Agriculture; Convolution; Convolutional neural networks; Principal component analysis; Spectroscopy; Agricultural land; Input vector; Overall accuracies; Principal Components; Spatial features; Spectral data; Image classification; agricultural land; article; case report; clinical article; convolutional neural network; principal component analysis; vegetation
Type
journal article