Structurization of synthetic aperture radar information by using neural networks
暂无分享,去创建一个
[1] Akira Hirose,et al. Adaptive Complex-Amplitude Texture Classifier that Deals with Both Height and Reflectance for Interferometric SAR Images , 2000 .
[2] Akira Hirose,et al. Quaternion Neural-Network-Based PolSAR Land Classification in Poincare-Sphere-Parameter Space , 2014, IEEE Transactions on Geoscience and Remote Sensing.
[3] Akira Hirose,et al. Averaged Stokes Vector Based Polarimetric SAR Data Interpretation , 2015, IEEE Transactions on Geoscience and Remote Sensing.
[4] Akira Hirose,et al. Unsupervised Fine Land Classification Using Quaternion Autoencoder-Based Polarization Feature Extraction and Self-Organizing Mapping , 2018, IEEE Transactions on Geoscience and Remote Sensing.
[5] Akira Hirose,et al. Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[6] Akira Hirose,et al. Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars , 2017, Neurocomputing.
[7] Uwe Stilla,et al. Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks , 2016, IEEE Geoscience and Remote Sensing Letters.
[8] Akira Hirose,et al. PolSAR Wet Snow Mapping With Incidence Angle Information , 2016, IEEE Geoscience and Remote Sensing Letters.
[9] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.