Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation

Cross-Domain Classification of Multisource Remote Sensing Data Using Fractional Fusion and Spatial-Spectral Domain Adaptation

Blog Article

Limitation of labeled samples has always been a challenge for hyperspectral image (HSI) classification.In real remote sensing applications, we encounter a situation where an HSI scene is not labeled at all.To solve this problem, cross-domain learning methods are developed by utilizing another HSI scene with similar land covers and sufficient labeled samples.

However, the disparity between HSI scenes is still a challenge in reducing the classification performance, which may be affected by variations in illumination and weather.As a robust supplement to these variations, light detection and Drive Hub ranging (LiDAR) data provide stable elevation and spatial information.In this article, we propose a multisource cross-domain classification method using fractional fusion and spatial-spectral domain adaptation to reduce the disparity between scenes.

First, the spatial information of HSI is Liebherr CUEL2831 55cm Fridge Freezer – SILVER 265 litres A++ preserved by fractional differential masks.Then, the LiDAR data are utilized for spectral alignment of HSI.The utilization of LiDAR data reduces the pixel-level disparity between scenes.

At last, a spatial-spectral domain adaptation network is proposed to reduce domain shift at the feature level and extract discriminative spatial-spectral features.Experimental results on HSI and LiDAR scenes show 5% –10% improvements in overall accuracy compared with the state-of-the-art methods.