Conditional generative adversarial networks (cGANs) have dominated the research of synthetic aperture radar (SAR)-to-optical (S2O) image translation, attributing to the feature generalization ability of residual convolutional blocks. Nevertheless, the fixed geometric structures of convolutional kernels hinder the feature inference from local to global, resulting in unclear contours and missing details in the generated image. To address this challenge, we proposed a contextual inference transformer block in the generator, dubbed CoIT. It enables the network to capture key features in SAR images through context awareness, providing a more comprehensive feature representation from local details to global structure. The proposed CoIT block contextually encodes input keys to establish the relationship between SAR images and optical images, improving the quality of generated images. Experiments on the public datasets WHU-SEN-City and SEN12MS show that the proposed method not only achieves better visual effects but also makes certain progress in evaluation indicators.

Polar sea ice monitoring is essential for climate change analysis and ship navigation route security. The current urgent problem to be solved is how to build a unified sea ice parameter inversion framework to obtain abundant and robust ice charts. However, most machine learning methods are merely suitable for modeling sea ice classification tasks under a single data source. Thus, a multitask sea ice inversion approach is proposed to address this challenge. First, SAR data and microwave scanning radiometer-2 (AMSR-2) data are jointly employed for multitask inversion, e.g., sea ice concentration (SIC), the stages of sea ice development (SOD), and sea ice floe size (FLOE). Then, the proportional-integral-derivative (PID) controllers are introduced into a three branch convolutional neural network to parse detailed, context and boundary information of the sea ice scene, respectively. Finally, three classifiers assign the multilabels to each of tasks for the parameter inversion. Experiments conducted on the AI4Arctic Sea Ice dataset show that our proposal outperforms typical CNN-based methods.

In this letter, we propose the CLN-Fusion, a novel hybrid fusion approach that leverages the merits of CNNs and vision Transformers (ViTs) to couple local-nonlocal feature representations between SAR and MS images. Specifically, we construct a paired token projection (PTP) to match the observation scenario content consistency of the two. Meanwhile, in terms of merging the complementary features between structures in SAR images and textures in MS images, we establish the pyramid CNN and ViT branches that assemble two pure feature volumes with convolutional inductive biases and nonlocal statistical correlation respectively into a mixed one. Furthermore, our CLN-Fusion maintains semantic alignment by maximizing mutual information throughout the PTP. Extensive experiments validate the superiority of the CLN-Fusion in terms of quantitative metrics, achieving SAR/MS image fusion under three scenarios from Sentinel-1 and Landsat8 data. with PSNRs of 33.1565, 30.9815, and 29.9821, showcasing the utmost fusion performance in contrast to other state-of-the-art (SOTA) methods. The codes of this work will be available at https://github.com/Blueseatear/CLN-Fusion.

Transformer networks have shown impressive performance for hyperspectral interpretation. Nevertheless, the high-dimensional redundant spectral distribution of hyperspectral images (HSIs) hinders their validity of interaction between features from distant locations. In this letter, we propose the HSI-Mixer, a novel extremely simple convolution neural network (CNN), which is similar in spirit to Transformer to re-consider the remarkable inductive biases of convolutions. In specific, we construct a hybrid measurement-based linear projection (HMLP) to merge spectral signatures and spatial positions of an HSI cuboid. Meanwhile, according to the merging relations between spectral-spatial attributes, we establish both spectral and spatial Mixer blocks to separate features from a mixed volume to a pure one, across either spectral bands or spatial locations, respectively. Furthermore, our HSI-Mixer maintains the same-depth-and-resolution throughout the network. Experimental results on three benchmark datasets demonstrate that our proposal achieves promising performance, in contrast to other state-of-the-art methods. The codes of this work will be available at https://github.com/Blueseatear/IEEE_GRSL_2022_HSI-Mixer.

This letter proposes a two-phase strategy-based grouped collaborative representation classifier (CRC) for hyperspectral image classification. Specifically, a spectral correlation-based CRC (SCCR) is proposed in the first phase, which considers a regularization term that uses the spectral correlations between the test sample and training samples. The representation coefficient vector generated by SCCR is then transformed into class-specific group weights. In the second phase, we integrate group weights and spectral correlations into the CRC and propose a grouped CRC (GCRC). Experimental results obtained from three real hyperspectral data sets demonstrate that the proposed SCCR and GCRC can provide better classification performance over other state-of-the-art representation-based classifiers.

Hyperspectral image (HSI) is usually composed of hundreds of continuous bands, leading a challenge task for pixel-level classification owing to high-dimensional spectral features and insufficient labeled samples. In this paper, an adaptive neighborhood strategy based generative adversarial network with (AN-GAN) for semi-supervised HSI classification is proposed. The proposed AN-GAN approach firstly uses superpixel algorithm, e.g., simple linear iterative clustering (SLIC), to generate multiple spatially homogeneous regions. Furthermore, each superpixel is merged with its spectrally similar neighbor superpixels. Then, for the reconstructed superpixels, the limited labeled samples are used to train discriminator, and a large number of unlabeled samples are utilized to generate noise using sparse autoencoder and also used to train discriminator for purpose of improving discriminator performance. Experiments were conducted on both Pavia University and Indian Pines datasets, which show that AN-GAN could provide better classification performance comparing with state-of-the-art classification models.

Brain-Machine Interface (BMI) has emerged as a powerful tool for assisting disabled people and for augmenting human performance. Up so far, no studies have succeeded in the power augmentation for the multi-DOFs robot based on EEG signals, especially for the complex shoulder joint. In this work, we propose an electromyography (EMG) estimation method based on electroencephalography (EEG) signals to realize the power assistance. The positions of the electrodes where the motion information of shoulder joint is effectively and exactly extracted are discussed, and a linear model that correlates the EMG to the EEG signal is constructed utilizing motion-related features extracted from multi-location EEG measurements. The constructed model is used to estimate the human muscular activity of shoulder joint from EEG using Principal Component Analysis (PCA) method. The proposed approach is experimentally verified, and an average correlation coefficients are as high as about 0.90 for different subjects are obtained between the estimated and the actually measured EMG signal. Our results suggest that the estimation of EMG based on EEG is feasible. This demonstrates the potential of using EEG signals to support human activities via brain-machine interface.

The traction force of any moving object on a floor is the frictional force between the object and the floor. Therefore, investigation of the frictional constraint is very important for such moving object on a floor as well as a biped walking robot. Conventionally, the study of frictional constraint of a biped robot was only constrained in translational slip, without considering rotational slip, which is important in actual biped walking. This is one of the causes the walking speed of biped robots lower than the one of human beings. In this paper, an evaluation method of frictional constraints for biped robots is proposed. The translational friction force and the twist frictional torque acting on the biped robot's sole are both deduced and verified for single support phase. First, based on a biped robot model, an approach to calculate the frictional force and torque is deduced as the quantitative expression of frictional constraint. Then, the experimental verification method is designed and the proposed calculation approach is confirmed. Finally, the consideration of the result of experiments is discussed. This approach is expected to be used for helping the realization of fast biped walkings.