PolSAR Image Classification Based on Deep Convolutional Neural Networks Using Wavelet Transformation
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Shallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN models utilized in PolSAR image classification. In this letter, we propose using the Haar wavelet transform in deep CNNs for effective feature extraction to improve the classification accuracy of PolSAR imagery. Based on the results, the proposed deep CNN model obtained better average accuracy in the San Francisco region with an accuracy of 93.3% and produced more homogeneous classification maps with less noise compared to the two much shallower CNN models of AlexNet (87.8%) and a 2-D CNN network (91%). The proposed algorithm is efficient and may be applied over large areas to support regional wetland mapping and monitoring activities using PolSAR imagery. The codes are available at (https://github.com/aj1365/DeepCNN_Polsar).
Açıklama
Anahtar Kelimeler
Feature extraction, Convolutional neural networks, Image classification, Training data, Training, Scattering, Synthetic aperture radar, Convolutional neural network (CNN), deep learning, Haar transform, land cover mapping, polarimetric synthetic aperture radar (PolSAR)
Kaynak
Ieee Geoscience and Remote Sensing Letters
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
19
