Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework for Hyperspectral Image Classification

Hyperspectral images (HSIs) collect rich spatial-spectral information in hundreds of spectral bands, which are captured by hyperspectral remote sensors, (Rasti et al., IEEE Geosci Remote Sens Mag 8(4):60–88, 2020; Ghamisi et al., IEEE Geosci Remote Sens Mag 5(4):37–78, 2017; Peng et al., IEEE Trans Geosci Remote Sens 57(2):1183–1194, 2018; Lu et al., IEEE Trans Geosci Remote Sens 56(4):2183–2195, 2018), which are more effective in distinguishing different land-covers compared with other multispectral (Zhao et al., IEEE Trans Neural Netw Learn Syst 30(11):3212–3232, 2019) or RGB (red, green, and blue) image (Hong et al., IEEE Geosci Remote Sens Mag 11:4051, 2021). Therefore, HSIs are widely employed in various applications, ranging from military reconnaissance, marine monitoring to disaster prevention and control (Hong et al., IEEE Geosci Remote Sens 9:16820, 2020; Ding et al., IEEE Geosci Remote Sens Lett 19:1–5, 2021). HSI classification (category each pixel into certain label) is a crucial technique for these applications. However, the complex noise effects and spectral variability (Hong et al., IEEE Trans Image Process 28(4):1923–1938, 2019), labeled training samples deficiency (Wan et al., IEEE Trans Geosci Remote Sens 59(1):597–612, 2021), and high spectral mixing between materials (Zhong et al., IEEE Trans Neural Netw Learn Syst 14:1–13, 2019) bring difficulties in extracting discriminative information from HSI for classification.