Scene classification of high spatial resolution (HSR) images can provide data support for many practical applications, such as land planning and utilization, and it has been a crucial research topic in the remote sensing (RS) community. Recently, deep learning methods driven by massive data show the impressive ability of feature learning in the field of HSR scene classification, especially convolutional neural networks (CNNs). Although traditional CNNs achieve good classification results, it is difficult for them to effectively capture potential context relationships. The graphs have powerful capacity to represent the relevance of data, and graph-based deep learning methods can spontaneously learn intrinsic attributes contained in RS images. Inspired by the abovementioned facts, we develop a deep feature aggregation framework driven by graph convolutional network (DFAGCN) for the HSR scene classification. First, the off-the-shelf CNN pretrained on ImageNet is employed to obtain multilayer features. Second, a graph convolutional network-based model is introduced to effectively reveal patch-to-patch correlations of convolutional feature maps, and more refined features can be harvested. Finally, a weighted concatenation method is adopted to integrate multiple features (i.e., multilayer convolutional features and fully connected features) by introducing three weighting coefficients, and then a linear classifier is employed to predict semantic classes of query images. Experimental results performed on the UCM, AID, RSSCN7, and NWPU-RESISC45 data sets demonstrate that the proposed DFAGCN framework obtains more competitive performance than some state-of-the-art methods of scene classification in terms of OAs.

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose the proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm called spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, SSMRPE utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjust the reconstruction weights to improve the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on the PaviaU and Salinas hyperspectral data sets indicate that SSMRPE can achieve better classification results in comparison with some state-of-the-art methods.

Scene classification is an indispensable part of remote sensing image interpretation, and various convolutional neural network (CNN)-based methods have been explored to improve classification accuracy. Although they have shown good classification performance on high-resolution remote sensing (HRRS) images, discriminative ability of extracted features is still limited. In this letter, a high-performance joint framework combined CNNs and vision transformer (ViT) (CTNet) is proposed to further boost the discriminative ability of features for HRRS scene classification. The CTNet method contains two modules, including the stream of ViT (T-stream) and the stream of CNNs (C-stream). For the T-stream, flattened image patches are sent into pretrained ViT model to mine semantic features in HRRS images. To complement with T-stream, pretrained CNN is transferred to extract local structural features in the C-stream. Then, semantic features and structural features are concatenated to predict labels of unknown samples. Finally, a joint loss function is developed to optimize the joint model and increase the intraclass aggregation. The highest accuracies on the aerial image dataset (AID) and Northwestern Polytechnical University (NWPU)-RESISC45 datasets obtained by the CTNet method are 97.70% and 95.49%, respectively. The classification results reveal that the proposed method achieves high classification performance compared with other state-of-the-art (SOTA) methods.

Marginal Fisher analysis (MFA) exploits the margin criterion to compact the intraclass data and separate the interclass data, and it is very useful to analyze the high-dimensional data. However, MFA just considers the structure relationships of neighbor points, and it cannot effectively represent the intrinsic structure of hyperspectral imagery (HSI) that possesses many homogenous areas. In this paper, we propose a new dimensionality reduction (DR) method, termed local geometric structure Fisher analysis (LGSFA), for HSI classification. Firstly, LGSFA uses the intraclass neighbor points of each point to compute its reconstruction point. Then, an intrinsic graph and a penalty graph are constructed to reveal the intraclass and interclass properties of hyperspectral data. Finally, the neighbor points and corresponding intraclass reconstruction points are used to enhance the intraclass-manifold compactness and the interclass-manifold separability. LGSFA can effectively reveal the intrinsic manifold structure and obtain the discriminating features of HSI data for classification. Experiments on the Salinas, Indian Pines, and Urban data sets show that the proposed LGSFA algorithm achieves the best classification results than other state-of-the-art methods.

Scene classification is an active research topic in the remote sensing community, and complex spatial layouts with various types of objects bring huge challenges to classification. Convolutional neural network (CNN)-based methods attempt to explore the global features by gradually expanding the receptive field, while long-range contextual information is ignored. Vision transformer (ViT) can extract contextual features, but the learning ability of local information is limited, and it has a large computational complexity simultaneously. In this article, an end-to-end method is exploited by employing ViT as an excellent teacher for guiding small networks (ET-GSNet) in the remote sensing image scene classification. In the ET-GSNet, ResNet18 is selected as the student model, which integrates the superiorities of the two models via knowledge distillation (KD), and the computational complexity does not increase. In the KD process, the ViT and ResNet18 are optimized together without independent pretraining, and the learning rate of teacher model gradually decreases until zero, while the weight coefficient of the KD loss module is doubled. Based on the above procedures, dark knowledge from the teacher model can be transferred to the student model more smoothly. Experimental results on the four public remote sensing datasets demonstrate that the proposed ET-GSNet method possesses the superior classification performance compared to some state-of-the-art (SOTA) methods. In addition, we evaluate the ET-GSNet on a fine-grained ship recognition dataset, and the results show that our method has good generalization for different tasks in terms of some metrics.

The scene classification of high spatial resolution (HSR) images is a challenging task in the remote sensing community. How to construct a discriminative representation of the HSR scene is a key step to improve classification performance. In this letter, we propose a novel feature extraction method termed multilayer feature fusion network (MF²Net) for scene classification. At first, the transferred VGGNet-16 model is employed as a feature extractor to acquire multilayer convolutional features. Then, several layers including pooling, transformation, and fusion layers are designed to process hierarchical features in four branches, and the prediction probability can be obtained for classification. Finally, the proposed model is optimized by fine-tuning techniques, where a novel data augmentation approach is explored to improve generalization ability. As a result, MF²Net effectively applies useful information from multilayers to improve the accuracy of scene classification. The experimental results on AID and NWPU-RESISC45 data sets exhibit that the MF²Net method obtains quite competitive classification results compared with many state-of-the-art methods.