The essence of human-like trajectory learning is to construct correspondences between scene elements and temporal trajectory points. Extracting key scene features and setting proper guidance during the learning process are crucial to improving the accuracy of human-like trajectory learning. Therefore, this paper proposes a graph feature extraction method for unstructured scene elements combined with a learning-based two-stage trajectory planner for human-like trajectory generation. The construction of the graph structure considers environmental, trajectory, and waypoint features, with environmental features specifically constructed through pixel clustering and motion compensation to enhance efficiency. In the first stage of the dual-phase trajectory planning, feature extraction is performed using Spatial-Temporal Graph Convo-lutional Networks (ST-GCN), followed by proposal trajectory generation with a sequence to sequence(Seq2Seq) network. In the second stage, the proposed trajectory from the first stage serves as the input, with predicted goal points obtained through the Multilayer Perceptron(MLP) network. The final trajectory is then generated by fusing graph and guidance features. The results demonstrate that the proposed scene graph structure effectively reduces the complexity of the learning network, thereby improving algorithm efficiency. Additionally, heatmap-guided features, jointly generated with the learned predicted goal points and the regularization method, effectively guide trajectory generation and improve the accuracy of human-like trajectory generation.

Remote-sensing image super-resolution (SR) reconstructs high resolution (HR) with texture from the input low resolution (LR). It has been widely used and applied in image-processing tasks. However, most algorithms focus on designing more complex structures to enhance performance, ignoring learning frequency information. Moreover, existing methods are designed for SR tasks with specific scales, such as scales of 2 and 4. It limits the network performance in applications. To alleviate the above issues, this letter designs a multilevel wavelet learning network (MWLN) for continuous-scale remote-sensing image SR. MWLN achieves continuous magnification remote-sensing image SR tasks without training at different scales multiple times through multilevel wavelet feature aggregation (MWFA) and self-learning implicit representation (SLIR). MWFA extracts hierarchical features and applies discrete wavelet transforms (DWTs), capturing high-frequency information while avoiding information loss. Moreover, this letter cascades a multidimensional attention mechanism model channel and spatial features and enhances features’ interaction. SLIR maps the image coordinates and red, green, and blue (RGB) value through self-learning, realizing the continuous-scale reconstruction. Extensive experimental results demonstrate that MWLN outperforms the compared methods in quantitative and qualitative results on specific and continuous-scale remote-sensing image SR tasks.

Recently, few-shot scene classification has become an important task in the remote sensing (RS) field, mainly solving how to obtain better classification performance when there are insufficient labeled samples. The few-shot scene classification task includes the pretrain stage and meta-test stage. There is no category intersection between these two stages. Thus, the sample distribution of the training set and meta-test set is different, leading to the training model’s weak generalization or portability. To solve this problem, we propose a class-centralized dictionary learning (CCDL) method for the few-shot RS scene classification (FSRSSC). Specifically, in the pretraining stage, we adopt the model pretrained on a large natural images dataset and then fine-tune the network by the RS dataset. Using the pretrained model helps improve the model’s generalization ability. In the meta-test stage, we propose a CCDL classifier, which guarantees the sparse representations of different categories more distant and the same more concentrated. We experiment on several benchmark datasets and achieve superior performance, demonstrating the proposed method’s effectiveness.

The remote sensing (RS) image super-resolution (SR) algorithm aims to reconstruct a high-resolution (HR) image with rich texture details from a given low-resolution (LR) image, improving the spatial resolution. It has been widely concerned in remote sensing image processing and application. Most current deep learning-based methods rely on paired training datasets. However, most datasets are often based on bicubic degradation. This single construction way limits the performance of the pre-trained network. Moreover, SR is an ill-posed problem in that multiple SR images are constructed from a single LR input. This paper proposes a Region-Aware Network (RAN) for remote sensing image super-resolution to alleviate the above issues. First, we introduce the contrastive learning strategy to mine the latent degraded representation of the image and serve as the prior knowledge of the network. Considering the RS images are acquired in specific scenes that have apparent self-similarity. Then, we propose a Region-Aware Module (RAM) based on attention mechanisms and the graph neural network to explore region information and cross-patch self-similarity. Extensive experiments have demonstrated that the proposed RAN adapts to RS image super-resolution tasks with various degradations and performs better in constructing texture information.

In recent years, deep learning-based remote-sensing image super-resolution (SR) methods have made significant progress, and these methods require a large number of synthetic data for training. To obtain sufficient training data, researchers often generate synthetic data via fixed bicubic downsampling methods. However, the synthesized data cannot reflect the complex degradation process of real remote-sensing images. Thus, performance will dramatically reduce when these methods work in real low-resolution (LR) remote-sensing images. This letter proposes a U-shaped attention connection network (US-ACN) for remote-sensing image SR to solve this issue. Our US-ACN does not rely on any synthetic external dataset for training and merely requires one LR image to complete the training. The US-ACN utilizes remote-sensing images’ strong internal feature repetitiveness and fully learns this internal repetitive feature through a well-designed US-ACN to achieve the remote-sensing image SR. In addition, we design a 3-D attention module to generate effective 3-D weights by modeling channel and spatial attention weights, which is more helpful for the learning of internal features. Through the U-shaped connection among attention modules, context information propagation and attention weights learning are fully utilized. Many experiments show that our US-ACN adequately adapts to the remote-sensing image SR in various situations and performs advanced performance.

In the field of remote sensing, it is infeasible to collect a large number of labeled samples due to imaging equipment and the imaging environment. Few-Shot Learning (FSL) is the dominant method to alleviate this problem, which pursues quickly adapting to novel categories from a limited number of labeled samples. The few-shot Remote Sensing Scene Classification (RSSC) generally includes the pre-training and meta-test phases. However, a “negative transfer” problem exists that data categories in both phases are different. It causes the pre-trained feature extractor to be unable well-adapted to the novel data category. This paper proposes Class Shared Dictionary Learning for Few-Shot Remote Sensing Scene Classification (CSDL) to address this issue. Specifically, this paper designs the Mirror-based Feature Extractor (MFE) in the pre-training phase, constructing a self-supervised classification task to improve the feature extractor robustness. Furthermore, this paper proposes a Class Shared Dictionary classifier (CSD) based on dictionary learning. The CSD projects the novel data feature in meta-test into subspace to reconstruct more discriminative features and complete the classification task. Extensive experiments on remote sensing datasets have demonstrated that the proposed CSDL achieves the advanced classification performance.

Few-shot learning (FSL), purposing to resolve the problem of data-scarce, has attracted considerable attention in recent years. A popular FSL framework contains two phases: (i) the pre-train phase employs the base data to train a CNN-based feature extractor. (ii) the meta-test phase applies the frozen feature extractor to novel data (novel data has different categories from base data) and designs a classifier for recognition. To correct few-shot data distribution, researchers propose Semi-Supervised Few-Shot Learning (SSFSL) by introducing unlabeled data. Although SSFSL has been proved to achieve outstanding performances in the FSL community, there still exists a fundamental problem: the pre-trained feature extractor cannot adapt to the novel data flawlessly due to the cross-category setting. Usually, large amounts of noises are introduced to the novel feature. We dub it as Feature-Extractor-Maladaptive (FEM) problem. To tackle FEM, we make two efforts in this paper. First, we propose a novel label prediction method, Isolated Graph Learning (IGL). IGL introduces the Laplacian operator to encode the raw data to graph space, which helps reduce the dependence on features when classifying, and then project graph representation to label space for prediction. The key point is that: IGL can weaken the negative influence of noise from the feature representation perspective, and is also flexible to independently complete training and testing procedures, which is suitable for SSFSL. Second, we propose Graph Co-Training (GCT) to tackle this challenge from a multi-modal fusion perspective by extending the proposed IGL to the co-training framework. GCT is a semi-supervised method that exploits the unlabeled samples with two modal features to crossly strengthen the IGL classifier. We estimate our method on five benchmark few-shot learning datasets and achieve outstanding performances compared with other state-of-the-art methods. It demonstrates the effectiveness of our GCT.