Abstract
In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.
Highlights
With the continuous expansion of remote sensing image applications, the demand for hyperspectral imaging (HSI) applications is increasing, especially in the domains of land cover classification, specific target detection and recognition, environmental monitoring, and precision agriculture [1,2,3,4]
The TRS-DCHC utilizes a training strategy that combines real and simulated data and adds discrete wavelet transform (DWT) to the network to explicitly decompose stripe patterns and stripe-free patterns, which is beneficial for improving the destriping performance
For a more intuitive discussion on whether we can improve the performance of the network in the actual application process after incorporating real data, we compare the destriping performances of DCHC and TRS-DCHC using another set of Zhuhai-1 data as the experimental input to evaluate the effectiveness of the real data training strategy
Summary
With the continuous expansion of remote sensing image applications, the demand for hyperspectral imaging (HSI) applications is increasing, especially in the domains of land cover classification, specific target detection and recognition, environmental monitoring, and precision agriculture [1,2,3,4]. HSI data have many bands, and are highly susceptible to interference during the imaging process by a series of degradation phenomena, such as thermal noise, impulse noise, stripe noise, and dead lines. These factors cause numerous adverse effects on his data processing, and the real spectral information is critically damaged, which constrains the application of HSI data. Of the many degradation factors, stripe noise is a common and special type of noise that usually has line characteristics. For removal of the stripe noise, many methods have been proposed. We divided the stripe removal methods into two groups: model-driven destriping method, and data-driven destriping method
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