HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery
In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection d...
Main Authors: | Gaigai Zhang, Shizhi Zhao, Wei Li, Qian Du, Qiong Ran, Ran Tao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-05-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/9/1489 |
Similar Items
-
A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
by: Geoffrey A. Fricker, et al.
Published: (2019-10-01) -
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
by: Somayeh Nezami, et al.
Published: (2020-03-01) -
Hyperspectral Image Classification Based on a Shuffled Group Convolutional Neural Network with Transfer Learning
by: Yao Liu, et al.
Published: (2020-06-01) -
A Hyperspectral Target Detection Framework With Subtraction Pixel Pair Features
by: Jinming Du, et al.
Published: (2018-01-01) -
Fine-Grained Classification of Hyperspectral Imagery Based on Deep Learning
by: Yushi Chen, et al.
Published: (2019-11-01)