SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification

by   Yun Cao, et al.

Subspace learning (SL) plays an important role in hyperspectral image (HSI) classification, since it can provide an effective solution to reduce the redundant information in the image pixels of HSIs. Previous works about SL aim to improve the accuracy of HSI recognition. Using a large number of labeled samples, related methods can train the parameters of the proposed solutions to obtain better representations of HSI pixels. However, the data instances may not be sufficient enough to learn a precise model for HSI classification in real applications. Moreover, it is well-known that it takes much time, labor and human expertise to label HSI images. To avoid the aforementioned problems, a novel SL method that includes the probability assumption called subspace learning with conditional random field (SLCRF) is developed. In SLCRF, first, the 3D convolutional autoencoder (3DCAE) is introduced to remove the redundant information in HSI pixels. In addition, the relationships are also constructed using the spectral-spatial information among the adjacent pixels. Then, the conditional random field (CRF) framework can be constructed and further embedded into the HSI SL procedure with the semi-supervised approach. Through the linearized alternating direction method termed LADMAP, the objective function of SLCRF is optimized using a defined iterative algorithm. The proposed method is comprehensively evaluated using the challenging public HSI datasets. We can achieve stateof-the-art performance using these HSI sets.



There are no comments yet.


page 1

page 2

page 8

page 9

page 10

page 11


Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Image segmentation is considered to be one of the critical tasks in hype...

Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification

In this paper, we address the hyperspectral image (HSI) classification t...

Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images

Spectral-spatial classification of remotely sensed hyperspectral images ...

Hyperspectral Image Classification in the Presence of Noisy Labels

Label information plays an important role in supervised hyperspectral im...

Image Steganography using Gaussian Markov Random Field Model

Recent advances on adaptive steganography show that the performance of i...

Planecell: Representing the 3D Space with Planes

Reconstruction based on the stereo camera has received considerable atte...

Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning

In this work, a novel method based on the learning using privileged info...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.