Hyperspectral Image Intensity Adaptive Destriping Method Based on Reference Image-Guided Pixel Clustering

Abstract

Hyperspectral images (HSIs) have widespread applications in geoscience, environmental monitoring, and resource management. However, in practical engineering applications, random stripe noise in HSIs severely affects data quality and accuracy, impacting the subsequent use of HSIs. In this article, we propose an HSI intensity adaptive destriping method based on reference image-guided pixel clustering. The proposed method removes stripe noise through three main stages. First, the HSI is analyzed to manually select a specific band image with minimal impact from random stripe noise. This image is designated the initial reference image, and its specific location is identified. Second, the proposed pixel threshold classification method, deep maximum interclass variance (DMIV), is used for pixel threshold classification for band images that are contaminated with random stripe noise and adjacent to the reference image. Finally, the resulting pixels are classified into two categories according to noise intensity: high intensity (HIN) and low intensity (LIN). The proposed intensity adaptive grayscale value reconstruction algorithm is used to remove stripe noise from each pixel category, and the denoised image is updated as a new reference image. Starting from the initial reference image, these steps are repeated along the spectrum to achieve destriping of all bands. We compare the proposed method against traditional and deep learning methods using real HSIs. The experimental results show that after denoising of the image using our method, both visual quality and quantitative evaluation metrics are significantly improved, with particularly excellent stripe removal performance observed for HSIs with significant grayscale variation.