Light field (LF) cameras are capable of capturing the direction and intensity of light, acquiring scene information from multiple viewpoints in a single shot. However, the trade-off between spatial and angular resolution in plenoptic cameras limits their application in scenarios requiring high spatial resolution. In recent years, Convolutional Neural Networks (CNN) have been widely applied to light field image processing, aiming to fully utilize the spatial and angular information of images. This paper proposes a novel CNN-based method for image information interaction in light field super-resolution (LFSR). This method effectively leverages the feature correlations between different views and promotes the learning of cross-scale and shallow features by introducing residual connections. The network architecture consists of three main stages: feature extraction, information interaction and fusion, and super-resolution (SR) reconstruction. Specifically, we extract rich spatial and angular features from the images, use residual connections in the information interaction and fusion stage to enhance feature learning capabilities, and finally upsample the fused features to generate the desired high-resolution (HR) images. Experimental results demonstrate that our method not only performs well in visual effects and quantitative evaluations but also accurately recovers the details in the reconstructed images.

Recently auxiliary cross-scene information has been widely utilized to improve the hypersperctral image classification performance by knowledge transfer. However, recognition of different objects with the same semantic category is difficult when the object types in similar scenes are different or only limited similarity knowledge is provided. In this paper, a multi-modal feature interactive learning (MMFI) method is proposed based on both hyperspectral image modality and textual modality to distinguish similar objects, which enhances the transfer capability by utilizing the semantic prior from the textual modality. First, the adversarial domain mapping (ADM) module is designed to realize cross-domain knowledge transfer across different scenes in an adversarial learning manner. In particular, the noise is simulated as data distribution in different domains through domain mapping and aggregated with source and target domain data, which is then reconstructed and optimized to learn discriminative and conducive information for transfer. Then, the adaptive interactive learning (AIL) module acts on the latent features of the encoder to mine latent associations among the aggregated features and facilitate the expression of consistent features. In addition, few-shot learning with textual embedding enables more powerful semantic priors for few-shot prototypes, making up for insufficient recognition capability in the presence of hyperspectral image modality only. Experimental results on three datasets demonstrate the superiority of our method.

There is a small sample problem in hyperspectral image (HSI) classification task due to the difficulty of labeling samples. It is generally solved using a combination of few-shot learning and cross-domain method. In the paper, we propose a domain-specific and domain-common feature enhancement method for cross-domain few-shot HSI classification. It consists of a domain adaptation module and a feature enhancement module. The former is used to learn domain-specific features of both domains from the beginning of the network, and the latter is used to reduce domain differences by learning domain-common features through feature enhancement. The experimental results indicate that our proposed method performs better than the advanced classification methods.

Hyperspectral image (HSI) classification is significant in remote sensing applications. However, most methods focus on the spectral and spatial correlation information in the neighborhood while ignoring the feature difference among global different pixels. In this article, we propose a spatial–spectral adaptive learning with pixelwise filtering (SSALPF) method to fully consider the discriminative information of pixels in different spatial locations, which mainly consists of a parallel spatial–spectral adaptive learning (SSAL) module and a pixelwise filtering (PF) module. Specifically, the former aims to obtain joint spatial–spectral discriminative features of each pixel point in a parallel manner and is used as a guide for adaptive selection of filter kernel. The latter uses the adaptive filter kernel to implement pixel-level filtering on HSI, in order to learn the discriminative features contained in different pixel points for classification. The adaptive filter kernel is generated by a linear combination of a predefined dictionary containing multiple filter bases. Experiments demonstrate that the proposed method is superior to other methods on popular hyperspectral datasets.

Recently, deep learning-based hyperspectral image (HSI) classification techniques are flourishing and exhibit good performance, where cross domain information is usually utilized to reduce the dependency on large labeled samples. However, the gap between source domain and target domain makes it difficult to carry out knowledge transfer directly. In this paper, an adversarial domain alignment with contrastive learning method is designed for the HSI classification task to achieve feature consistency that benefits transferring knowledge. In details, spectral alignment and semantic alignment are conducted in local and global levels respectively in an adversarial learning way, and the adversarial loss acts on both source and target domains. In order to learn specific features for objects with different spatial scales, a multi-scale selection module is constructed in semantic alignment to select channel features adaptively. Moreover, contrastive learning is employed to increase both robustness and sensitiveness, where augmented data from the same/different samples are forced to be similar/dissimilar with each other. The training process is conducted in a few-shot learning way then the few-shot classification loss, the adversarial loss and the contrastive loss is optimized together. Tested on one source dataset and four target datasets, the experimental results show that the proposed method outperforms the other comparisons.

Deep learning based semantic segmentation is one of the popular methods in remote sensing image segmentation. In this paper, a network based on the widely used encoder-decoder architecture is proposed to accomplish the synthetic aperture radar (SAR) images segmentation. With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images. These optical images can be used as expansions of original SAR images, thus ensuring robust result of segmentation. Then the optical images generated by the GAN are stitched together with the corresponding real images. An attention module following the stitched data is used to strengthen the representation of the objects. Experiments indicate that our method is efficient compared to other commonly used methods.

Recently, hyperspectral image (HSI) classification by deep learning is flourishing. However, only a few labeled samples are available in practice since it is time-and-labor-consuming to label pixels in HSI (called target domain). This paper proposes a domain-adaptive few-shot learning (DAFSL) method to tackle this problem. Specifically, some other HSIs (called source domain) with large labeled samples are fully used as complementary information and a generative architecture is employed to adapt embedded features in source domain to that of target domain. We first perform domain adaptation with unsupervised learning. In details, the embedded features are generated by the encoder of an autoencoder, where both source and target samples could be well recovered and the reconstruction loss is used to measure the gap between source domain and target domain. At the same time, the embedded features are put into a metric space for classification in source domain and the encoder parameter is fine-tuned together with the classifier in target domain with few labels, so that both general and discriminative features are well captured. The experiment results show that DAFSL outperforms the other mainstream methods with limited labeled samples.