Aiming at low spectral contrast materials, the Optimized Smoothing for Temperature Emissivity Separation (OSTES) method was developed to improve the Temperature and Emissivity Separation (TES) algorithm based on the linear relationship between brightness temperature and emissivity features, but there was little smoothing improvement for higher spectral contrast materials. In this paper, a new nonlinear-relationship based algorithm is presented, focusing on improving the performance of the OSTES method for materials with middle or high spectral contrast. This novel approach is a two-step procedure. Firstly, by introducing atmospheric impact factor, the nonlinear relationship is mathematically proved using first-order Taylor series approximation. Moreover, it is proven that nonlinear model has stronger universality than linear model. Secondly, a new method named Temperature and Emissivity Separation with Nonlinear Constraint (TESNC) is proposed based on the nonlinear model for smoothing temperature and emissivity retrieval. The key procedure of TESNC is the lowest emissivity smoothing estimation based on nonlinear model and retrieved by minimizing the reconstruction error of the Planck radiance. TESNC was tested on a series of synthetic data with different kinds of natural materials representing several multispectral and hyperspectral infrared sensors. It is shown that, especially for materials with higher spectral contrast, the proposed method is less sensitive to changes in atmospheric conditions and sample temperatures. Furthermore, the standard Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) products in different kind of atmospheric conditions were used for verifying the improvement. TESNC is more accurate and stable with the decrease of emissivity and changes of atmospheric conditions compared with TES, Adjusted Normalized Emissivity Method (ANEM), and OSTES methods.

The distinction of similar classes has always been the core issue in image classification. In this article, a new hierarchical classification process based on three-dimensional attention soft augmentation (HC-3DAA) is proposed to improve the accuracy of classifiers, especially for the accuracy between similar classes. In HC-3DAA processing, the merging matrix is first constructed based on the validation confusion matrix to measure the similarity among different classes. Specifically, the 3-D attention soft augmentation module combined with CutMix is designed for guiding the network model to focus on the key discriminative features. Then, the extracted 3-D feature differences between similar classes are inserted into the attention module for the reclassification to get higher classification accuracy. To evaluate the performance of HC-3DAA, CutMix models with different feature dimensions and the HC module are separately discussed on two widely used hyperspectral datasets. Two different classifiers 3-D convolutional neural network and ResNet are included in the comparative analysis. Besides, experimental results also demonstrate that the proposed HC-3DAA outperforms several state-of-the-art attention-based methods.

Backdoor attacks present significant risks to the security of deep neural networks (DNNs) in NLP domain, as the attackers can covertly manipulate the model

Compared with visible and near-infrared images, the long-wave infrared region hyperspectral image (LWIR HSI) is more vulnerable to noise pollution in the acquisition process due to its specific imaging mode. In this letter, a new restoration method is proposed using total variation based on bidirectional estimation and brightness temperature smoothing (BBSTV), which can remove dead lines and restore junk bands effectively. The proposed method introduces the linear relation between brightness temperature and emissivity derived from radiative transfer model (RTM) to restoration processing. Besides, bilateral estimation is used to complete the loss information of noise-polluted bands to achieve a faster convergence speed of total variation (TV) method. Both simulated and real LWIR HSI experiments were conducted to verify the improvements of the BBSTV method in quantitative and qualitative ways.