Stripe is a common degradation phenomenon in remote sensing images. The variation-based de-striping method, due to the defect of the model itself, always has an unnecessary influence on the stripe-free area while correcting the stripe, and cannot satisfy some requirements in high-precision quantitative applications or sensitive data processing of remote sensing images. This paper proposes a high-precision stripe correction method, which first detects the position of the stripes, and then uses the interpolation idea to correct the stripe to solve the fidelity problem of the stripe-free area in the de-striping process. We use the rational assumption that the derivative of the real signal in the stripe region (to be repaired) is consistent with the derivative of the observed signal, and then selects cubic Hermite spline interpolation method for de-striping, which can uses the derivative information of the region to be repaired (ie, the derivative information of the stripe region) to overcoming the difficulty of the existing interpolation de-stripe method not being able to work well when the stripes is too wide. The experimental results show that our method can effectively remove the stripes and maintain the stripe-free area intact.
Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, most of the classification methods have the problem of insufficient expression ability to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group CNN features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the "feature recalibration" strategy is used to recalibrate the spectral group CNN features. The experiment show that the proposed approach can improve the classification accuracy of categories with subtle differences.
Attention can be interpreted as a method which allocates available computing power to the most informative part of the signal. In deep learning, attention mechanism also helps us to dig out the subtle information. In hyperspectral classification, the discrimination of some land cover types depends on the fine differences of hyperspectral, but most classification methods do not focus on the fine differences between hyperspectral categories. In this paper, a hierarchical group attention classification method is proposed to focus on the differences of categories from coarse to fine, therefore, the fine differences between categories can be obtained to achieve more accurate classification. For comparison and validation, we test the proposed approach with three other classification approaches on Salinas and Indian datasets, and the experiments demonstrate that our proposed approach can distinguish the spectral subtle differences of similar categories more accurately.
Detecting underground target is important for national defense and security. Using the temperature field simulation, we can obtain the simulation model of the underground target. The data pattern of simulation is different from the data pattern of infrared remote sensing (RS), but the two patterns have a mapping relationship. We transform the data pattern of simulation to the data pattern of infrared RS, and then compare the transformed simulation data with the actual acquired infrared RS data to find the difference, so as to detect the underground target. Most of mappings of simulation data and infrared RS data have no sufficient robustness, and the mapping function is susceptible to external environmental factors. Using pix2pix model, a mapping approach is proposed to transform the simulation data to the infrared RS data. To evaluate this method, we take Deshengkou area of Beijing for experiment. Experiment shows that this mapping method has better robustness and adaptability.