Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …

Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …

With the advent of Internet, images and videos are the most vulnerable media that can be
exploited by criminals to manipulate for hiding the evidence of the crime. This is now easier …

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …

The performance of deep learning-based denoisers highly depends on the quantity and
quality of training data. However, paired noisy–clean training images are generally …

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the
subsequent HSI applications. Unfortunately, though witnessing the development of deep …

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease
in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability …

Automatic building extraction from high-resolution remote sensing imagery has various
applications, such as urban planning and land use management. However, the existing …

Timely pavement crack detection can prevent further pavement deterioration. However,
obtaining sufficient quantities of crack information at low cost remains a challenge. This …