Boosting Hyperspectral Image Classification with Dual Hierarchical Learning

Abstract

Hyperspectral image (HSI) classification aims at predicting the pixel-wise labels in an image, where there are only a few labeled pixel samples (hard labels) for training. It is a challenging task since the classification process is susceptible to over-fitting under training with limited samples. To relieve this problem, we propose a method based on dual hierarchical learning. First, we employ a connectionist hyperspectral convolution (HC) network to capture the representations of the pixels from different receptive fields. Specifically, an HC is designed to learn the correlation among adjacent pixels and is further extended to a connectionist hierarchical structure. These operations use the correlation to enhance one-pixel learning from multiple receptive fields. Second, we analyze the properties in the hyperspectral image and introduce a hierarchical pseudo label generation algorithm to enrich the supervision of the label information. Finally, we design a dual hierarchical learning strategy to help all HC layers learn from both the hard labels and the hierarchical pseudo labels. In other words, it addresses the HSI classification problem from different views. For inference, we employ two fusion strategies to find a better prediction. The experimental results on four popular HSI benchmarks, i.e. , Salinas-A, IndianPines, PaviaU, and PaviaC, demonstrate the effectiveness of the proposed method. Our code is publicly available on GitHub: https://github.com/ShuoWangCS/HSI-DHL.

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

“…In terms of time consumption, previous research has consistently found that a run time of ten is appropriate (Salehi et al, 2020 ). Furthermore, the standard deviation (SD) of average value is used to check the statistical property of accuracy for all ML algorithms for a robust and reliable algorithm consideration (Wang et al, 2022a , 2022b ). To report the results, Fig.…”

“…There are a few publications that investigate sustainable logistics and supply chain management from the perspective of supply chain network partners, such as suppliers, manufacturers, and customers, as well as the perspective of the innovative and intelligent supply chain, such as internet of things (IoT), big data, AI, and blockchain technology. Furthermore, rather than using real data from businesses, many publications use the quantitative (Likert scale) or qualitative scale (Fuzzy) method to collect data (Wang et al, 2022a , 2022b ). In this study, we develop a hybrid model in which we use the data envelopment analysis (DEA) technique to enhance the predictive power of different ML of linear and nonlinear techniques (linear regression and artificial neural network (ANN)) when dealing with few data in the domain of logistics and economics (e.g., LPI, microeconomic and macroeconomic data which biannually arrangement).…”