Hyperspectral Image Classification Based on Active Learning and Spectral-Spatial Feature Fusion Using Spatial Coordinates

Caihong Mu,Yi Liu,Yijin Liu,Jian Liu

doi:10.1109/access.2019.2963624

Caihong Mu, Yi Liu + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2963624

Copy DOI

Abstract

In Hyperspectral image (HSI) classification, combining spectral information with spatial information has become an efficient measure to obtain good classification results, where spatial information is generally introduced in an unsupervised way or some complicated way. We introduce spatial coordinates as the spatial information in a simple supervised way and propose two HSI classification algorithms, where spatial coordinates of samples are regarded as the spatial features of samples. A spectral-spatial classification algorithm is proposed, named as HSI Classification Based on Spectral-Spatial Feature Fusion using Spatial Coordinates (SSFFSC). The HSI is divided into multiple small images in spatial dimension, and samples in each small image are randomly selected as training samples. Support vector machine (SVM) is used to classify the samples to obtain the probability of samples belonging to each class according to the spatial coordinate features and spectral features respectively. The probability features are further classified by SVM to achieve the final classification result. Considering that the performance of SSFFSC relies on the partition of HSI, SSFFSC is further combined with active learning (AL) as a new method named as HSI Classification Based on Active Learning and SSFFSC (SSFFSC-AL). Partition of HSI is omitted and the training samples are selected adaptively by AL’s sampling scheme. We find spatial coordinates are useful spatial information. SSFFSC and SSFFSC-AL run fast and improve the classification accuracy effectively by using the spatial coordinates as the spatial features. Experiments demonstrate that comparing with other algorithms, SSFFSC and SSFFSC-AL can obtain higher classification accuracy in less time.

Highlights

Hyperspectral image (HSI) classification is important in hyperspectral remote sensing processing [1], [2]
All the rest of samples constitute the testing sample set. 2: Perform Principal component analysis (PCA) on spectral information and extract the first m principal components as spectral features. 3: Classify the samples based on spatial coordinates by support vector machine (SVM) to obtain the classification result and the probability value Pa corresponding to the result. 4: Classify the samples based on spectral information by SVM to obtain the classification result and the probability value Pe corresponding to the result. 5: Calculate the sampling measurement value W according to (4) using Pa and Pe. 6: Select some samples with the smallest W in the testing sample set and manually label these samples
With PSVM (Spectral Classification using PCA and SVM), SCSVM (Spatial Classification using Spatial Coordinates and SVM), Fractional-Order Darwinian Particle Swarm Optimization (FODPSO)-SVM (SVM classification combined with segmentation maps obtained by fractional-order Darwin particle swarm optimization algorithms) [14], and Partial Least Square (PLS)-ARSVM (Spatial information classification using information near the sample to correct the characteristics of the sample, and using partial least squares to reduce the size of the hyperspectral image.)

Summary

INTRODUCTION

Hyperspectral image (HSI) classification is important in hyperspectral remote sensing processing [1], [2]. 2) Considering the phenomenon that the performance of the algorithm based on spatial coordinates is related to the division of images, we introduce AL into the framework, and the proposed SSFFSC-AL can get better classification result when the size of training sample set is small. After the support vector machine (SVM) obtains the classification result, the Sigmoid fitting can get the probability distribution of the samples belonging to different categories [30], which makes it possible to design an approach of feature fusion of spatial coordinates and spectral information as follows. Where peik is obtained by the SVM classifier based on the spectral features and indicates the probability that the sample xi belongs to the class k (k = 1, 2, · · · , L) In this way, both spectral information and spatial coordinates are converted into probability feature vectors by SVM classifiers, making the fusion possible. Algorithm 1 shows the steps of hyperspectral image classification based on spectral-spatial feature fusion using spatial coordinates

COMBINED WITH ACTIVE LEARNING

HYPERSPECTRAL DATA SETS

CONCLUSION AND FUTURE RESEARCH