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

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.