Abstract The study of few-shot remote sensing image classification has received significant attention. Although meta-learning-based algorithms have been the primary focus of recent examination, feature fusion methods stress feature extraction and representation. Nonetheless, current feature fusion methods, like the multi-head mechanism, are restricted by their complicated network structure and challenging training process. This manuscript presents a simplified multi-head mechanism for obtaining multiple feature representations from a single sample. Furthermore, we perform specific fundamental transformations on remote-sensing images to obtain more suitable features for information representation. Specifically, we reduce multiple feature extractors of the multi-head mechanism to a single one and add an image transformation module before the feature extractor. After transforming the image, the features are extracted resulting in multiple features for each sample. The feature fusion stage is integrated with the classification prediction stage, and multiple linear classifiers are combined for multi-decision fusion to complete feature fusion and classification. By combining image transformation with feature decision fusion, we compare our results with other methods through validation tests and demonstrate that our algorithm simplifies the multi-head mechanism while maintaining or improving classification performance.

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.

Decoupled Few-shot learning (FSL) is an effective methodology that deals with the problem of data-scarce. Its standard paradigm includes two phases: (1) Pre-train. Generating a CNN-based feature extraction model (FEM) via base data. (2) Meta-test. Employing the frozen FEM to obtain the novel data features, then classifying them. Obviously, one crucial factor, the category gap, prevents the development of FSL, i.e., it is challenging for the pre-trained FEM to adapt to the novel class flawlessly. Inspired by a common-sense theory: the FEMs based on different strategies focus on different priorities, we attempt to address this problem from the multi-view feature collaboration (MVFC) perspective. Specifically, we first denoise the multi-view features by subspace learning method, then design three attention blocks (loss-attention block, self-attention block and graph-attention block) to balance the representation between different views. The proposed method is evaluated on four benchmark datasets and achieves significant improvements of 0.9%-5.6% compared with SOTAs.

Compared to deep learning methods, few-shot learning methods do not need many labeled images. Therefore, few-shot remote sensing image scene classification has been studied extensively. However, obtaining effect information from the limited amount of labeled samples is a great challenge. Most methods only extract features from a single perspective of remote sensing images. Such information is scarce and even misleading. To address the problem, we propose a multi-view feature fusion (MVFF) method. Specifically, first, train two feature extractor networks on the original image dataset and remote sensing image dataset, respectively. And each model extracts features before and after average pooling, and we obtain four kinds of features for a remote sensing image. Second, we calculate fusion weights from support set features using Multi-Head Feature Collaboration (MHFC) method and four classifiers. Third, we utilize the weights to fuse predictive probability matrices and thus obtain the labels of query set samples. We implement experiments on three benchmark remote-sensing image datasets to validate the performance of our method. And the results demonstrate that our approach effectively handles few-shot remote sensing image scene classification.

In recent years, few-shot remote sensing scene classification (FSRSSC) has attracted more and more attention. For FSRSSC, most methods currently focus on designing a meta-learning algorithm, which obtains meta-knowledge from limited samples and then applies it to novel tasks. In this work, on the one hand, we optimize the training pipeline of the feature extractor; on the other hand, we apply a novel model fusion method further to optimize the feature extractor capability of the feature extractor. We show a novel few-shot remote sensing scene classification baseline: learning two feature representations through using two self-supervised methods on the meta-training set and then fusing the two representations into one. Then, training a linear classifier on this representation achieves state-of-the-art performance. It shows that training a good feature extractor can be more efficient than complex meta-learning algorithms for FSRSSC. We believe that our results can inspire a rethinking of few-shot remote sensing scene classification benchmarks.

Recently, remote-sensing scene classification has become an essential primary research topic. Nowadays, scholars have proposed various few-shot remote-sensing scene classification methods to achieve superior performance with few labeled data. Most of the prior work utilized a meta-learning strategy, which suffered from too little data affecting performance. In this letter, we apply the pre-trained feature extractor for image embedding. Meanwhile, because of the negative transfer problem caused by the inadaptability of the pre-trained feature extractor to remote-sensing data, we propose to exploit two pre-trained models to classify the remote-sensing scene, respectively. Then we fuse the decision to obtain the final classification category. We design a decision attention module to automatically update combination weights for each decision. It comprehensively considers the contribution of various decisions and further improves the discrimination of features. We conduct comprehensive experiments to validate the method and achieve state-of-the-art performance on two benchmark remote-sensing scene datasets, namely NWPU-RESISC45 and UC Merced.

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.

In recent years, researchers pay growing attention to the few-shot learning (FSL) task to address the data-scarce problem. A standard FSL framework is composed of two components: i) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). ii) Meta-test. Apply the trained FEM to the novel data (category is different from base data) to acquire the feature embeddings and recognize them. Although researchers have made remarkable breakthroughs in FSL, there still exists a fundamental problem. Since the trained FEM with base data usually cannot adapt to the novel class flawlessly, the novel data’s feature may lead to the distribution shift problem. To address this challenge, we hypothesize that even if most of the decisions based on different FEMs are viewed asweak decisions, which are not available for all classes, they still perform decent in some specific categories. Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model. MDFM is a simple, flexible, non-parametric method that can directly apply to the existing FEMs. Besides, we extend the proposed MDFM to two FSL settings (e.g., supervised and semi-supervised settings). We evaluate the proposed method on five benchmark datasets and achieve significant improvements of 3.4%-7.3% compared with state-of-the-arts.

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.

The label-embedded dictionary learning (DL) algorithms generate influential dictionaries by introducing discriminative information. However, there exists a limitation: All the label-embedded DL methods rely on the labels due that this way merely achieves ideal performances in supervised learning. While in semi-supervised and unsupervised learning, it is no longer sufficient to be effective. Inspired by the concept of self-supervised learning (e.g., setting the pretext task to generate a universal model for the downstream task), we propose a Self-Supervised Dictionary Learning (SSDL) framework to address this challenge. Specifically, we first design a p-Laplacian Attention Hypergraph Learning (pAHL) block as the pretext task to generate pseudo soft labels for DL. Then, we adopt the pseudo labels to train a dictionary from a primary label-embedded DL method. We evaluate our SSDL on two human activity recognition datasets. The comparison results with other state-of-the-art methods have demonstrated the efficiency of SSDL.

Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). (2) Meta-test. Apply the trained FEM to acquire the novel data's features and recognize them. FSL relies heavily on the design of the FEM. However, various FEMs have distinct emphases. For example, several may focus more attention on the contour information, whereas others may lay particular emphasis on the texture information. The single-head feature is only a one-sided representation of the sample. Besides the negative influence of cross-domain (e.g., the trained FEM can not adapt to the novel class flawlessly), the distribution of novel data may have a certain degree of deviation compared with the ground truth distribution, which is dubbed as distribution-shift-problem (DSP). To address the DSP, we propose Multi-Head Feature Collaboration (MHFC) algorithm, which attempts to project the multi-head features (e.g., multiple features extracted from a variety of FEMs) to a unified space and fuse them to capture more discriminative information. Typically, first, we introduce a subspace learning method to transform the multi-head features to aligned low-dimensional representations. It corrects the DSP via learning the feature with more powerful discrimination and overcomes the problem of inconsistent measurement scales from different head features. Then, we design an attention block to update combination weights for each head feature automatically. It comprehensively considers the contribution of various perspectives and further improves the discrimination of features. We evaluate the proposed method on five benchmark datasets (including cross-domain experiments) and achieve significant improvements of 2.1%-7.8% compared with state-of-the-arts.