Panchromatic sharpening (pansharpening) is an important technology for improving the spatial resolution of multispectral (MS) images. The majority of the models are implemented at the reduced resolution, leading to unfavorable results at the full resolution. Moreover, the complicated relationship between MS and panchromatic (PAN) images is often ignored in detail injection. For the mentioned problems, unsupervised generative adversarial networks with recursive mixed-scale feature fusion for pansharpening (RMFF-UPGAN) are modeled to boost the spatial resolution and preserve the spectral information. RMFF-UPGAN comprises a generator and two U-shaped discriminators. A dual-stream trapezoidal branch is designed in the generator to obtain multiscale information. Further, a recursive mixed-scale feature fusion subnetwork is designed. Perform a prior fusion on the extracted MS and PAN features of the same scale. A mixed-scale fusion is conducted on the prior fusion results of the fine-scale and coarse-scale. The fusion is executed sequentially in the above manner building a recursive mixed-scale fusion structure and finally generating key information. A compensation information mechanism is also designed for the reconstruction of key information to compensate for information. A nonlinear rectification block for the reconstructed information is developed to overcome the distortion induced by neglecting the complicated relationship between MS and PAN images. Two U-shaped discriminators are designed and a new composite loss function is defined. The presented model is validated using two satellite data and the outcomes reveal better than the prevalent approaches regarding both visual assessment and objective indicators.

Multispectral (MS) pansharpening is crucial to improve the spatial resolution of MS images. MS pansharpening has the potential to provide images with high spatial and spectral resolutions. Pansharpening technique based on deep learning is a topical issue to deal with the distortion of spatio-spectral information. To improve the preservation of spatio-spectral information, we propose a novel three-stage detail injection pansharpening network (TDPNet) for remote sensing images. First, we put forward a dual-branch multiscale feature extraction block, which extracts four scale details of panchromatic (PAN) images and the difference between duplicated PAN and MS images. Next, cascade cross-scale fusion (CCSF) employs fine-scale fusion information as prior knowledge for the coarse-scale fusion to compensate for the lost information during downsampling and retain high-frequency details. CCSF combines the fine-scale and coarse-scale fusion based on residual learning and prior information of four scales. Last, we design a multiscale detail compensation mechanism and a multiscale skip connection block to reconstruct injecting details, which strengthen spatial details and reduce parameters. Abundant experiments implemented on three satellite data sets at degraded and full resolutions confirm that TDPNet trades off the spectral information and spatial details and improves the fidelity of sharper MS images. Both the quantitative and subjective evaluation results indicate that TDPNet outperforms the compared state-of-the-art approaches in generating MS images with high spatial resolution.

Images derived from an individual sensor fail to simultaneously satisfy the demands of high spatial, spectral, and temporal resolutions. Multisource remote sensing image fusion provides efficient access to high-spatial-resolution multispectral images (spatial spectral fusion, SSF) and high-temporal-spatial-resolution images (spatiotemporal fusion, STF). Whereas existing deep learning-based models can mainly implement either SSF or STF, there is an urgent need for models that can simultaneously implement both SSF and STF. A unified generative adversarial network with convolution and Transformer (CTUGAN) for SSF and STF is proposed. CTUGAN contains an adaptive-resolution generator (ACTG) and multiresolution discriminators (MCTDs), both with the convolution and Transformer. First, a bidirectional local-global feature encoder is devised in the ACTG to extract local-global features via a high-to-low resolution and a low-to-high resolution, respectively. Then, a multihead cross-attention fusion decoder is devised to aggregate and fuse complementary local-global features of various levels and resolutions hierarchically to restore valuable information. Moreover, MCTDs adversely learn multiresolution local-global features to identify the relative reality of products, and a generalized loss function is built to accomplish full supervision. Finally, numerous experiments on the SSF data (Gaofen-2 and QuikBird) and STF data (CIA and LGC) demonstrate that the proposed CTUGAN model outperforms both subjective and objective evaluations.

Purpose: The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil. Design/methodology/approach: Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula. Findings: It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value. Originality/value: The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.