Hyperspectral Image Classification via Spatial Window-Based Multiview Intact Feature Learning

2020 ◽  
pp. 1-13
Author(s):  
Yue Zhao ◽  
Yiu-ming Cheung ◽  
Xinge You ◽  
Qinmu Peng ◽  
Jiangtao Peng ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
Spatial Window

Hyperspectral Image Classification Algorithm Based on Principal Component Texture Feature Deep Learning

2020 ◽  
Vol 10 (9) ◽  
pp. 2027-2031
Author(s):  
Xu Yifang
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Texture Feature ◽  
Feature Learning ◽  
Principal Component ◽  
Classification Algorithm ◽  
Classification Problems ◽  
Hyperspectral Image Classification ◽  
Reconstruction Performance

Hyperspectral image classification refers to a key difficulty on the domain of remote sensing image processing. Feature learning is the basis of hyperspectral image classification problems. In addition, how to jointly use the space spectrum information is Also an important issue in hyperspectral image classification. Recent ages have seen that as further exploration is developing, the method of hyperspectral image cauterization according to deep learning has been rapidly developed. However, existing deep networks often only consider reconstruction performance while ignoring the task itself. In addition, for improving preciseness of classification, most categorization methods use the fixed-size neighborhood of per hyperspectral pixel as the object of feature extraction, ignoring the identification and difference between the neighborhood pixel and the current pixel. On the basis of exploration above, our research group put forward with an image classification algorithm based on principal component texture feature deep learning, and achieved good results.

Multiple Feature Learning for Hyperspectral Image Classification

2015 ◽  
Vol 53 (3) ◽  
pp. 1592-1606 ◽  
Author(s):  
Jun Li ◽  
Xin Huang ◽  
Paolo Gamba ◽  
Jose M. Bioucas Bioucas-Dias ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
Multiple Feature

scholarly journals Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 2033 ◽  
Author(s):  
Xiaofei Yang ◽  
Xiaofeng Zhang ◽  
Yunming Ye ◽  
Raymond Y. K. Lau ◽  
Shijian Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
3 Dimensional ◽  
Recent Success ◽  
Hybrid Module

Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs.

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