An effective global learning framework for hyperspectral image classification based on encoder-decoder architecture

Most deep learning methods in hyperspectral image(HSI)classification use local learning methods,where overlapping areas between pixels can lead to spatial redundancy and higher computational cost.This paper proposes an efficient global learning(EGL)framework for HSI classification.The EGL framework was composed of universal global random stratification(UGSS)sampling strategy and a classification model BrsNet.The UGSS sampling strategy was used to solve the problem of insufficient gradient variance resulted from limited training samples.To fully extract and explore the most distinguishing feature representation,we used the modified linear bottleneck structure with spectral attention as a part of the BrsNet network to extract spectral spatial information.As a type of spectral attention,the shuffle spectral attention module screened important spectral features from the rich spectral information of HSI to improve the classification accuracy of the model.Meanwhile,we also designed a double branch structure in BrsNet that extracted more abundant spatial information from local and global perspectives to increase the performance of our classification framework.Experiments were conducted on three famous datasets,IP,PU,and SA.Compared with other classification methods,our proposed method produced competitive results in training time,while having a greater advantage in test time.

Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.