Compared with natural scenes, aerial scenes are usually composed of numerous objects densely distributed within the aerial view, and thus, more key local semantic features are needed to describe them. However, when existing CNNs are used for remote sensing image classification, they typically focus on the global semantic features of the image, and especially for deep models, shallow and intermediate features are easily lost. This article proposes a channel–spatial attention mechanism based on a depthwise separable convolution (CSDS) network for aerial scene classification to solve these challenges. First, we construct a depthwise separable convolution (DS-Conv) and pyramid residual connection architecture. DS-Conv extracts features from each channel and merges them, effectively reducing the number of necessary calculations, and the pyramid residual connections connect the features from multiple layers and create associations. Then, the channel–spatial attention algorithm causes the model to obtain more effective features in the channel and spatial domains. Finally, an improved cross-entropy loss function is used to reduce the impact of similar categories on backpropagation. Comparative experiments on three public datasets show that the CSDS network can achieve results comparable to those of other state-of-the-art methods. In addition, visualization of feature extraction results by the Grad-CAM algorithm and ablation experiments for each module reflect the powerful feature learning and representation capabilities of the proposed CSDS network.

The spatial distribution of remote-sensing scene images is highly complex in character, so how to extract local key semantic information and discriminative features is the key to making it possible to classify accurately. However, most of the existing convolutional neural network (CNN) models tend to have global feature representations and lose the shallow features. In addition, when the network is too deep, gradient disappearance and overfitting tend to occur. To solve these problems, a lightweight, multi-instance CNN model for remote sensing scene classification is proposed in this paper: MILRDA. In the instance extraction and classifier part, more discriminative features are extracted by the constructed residual dense attention block (RDAB) while retaining shallow features. Then, the extracted features are transformed into instance-level vectors and the local information associated with bag-level labels is highlighted by the proposed channel-attention-based multi-instance pooling, while suppressing the weights of useless objects or backgrounds. Finally, the network is constrained by the cross-entropy loss function to output the final prediction results. The experimental results on four public datasets show that our proposed method can achieve comparable results to other state-of-the-art methods. Moreover, the visualization of feature maps shows that MILRDA can find more effective features.

Due to the rapid development of satellite technology, high‐spatial‐resolution remote sensing (HRRS) images have highly complex spatial distributions and multiscale features, making the classification of such images a challenging task. The key to scene classification is to accurately understand the main semantic information contained in images. Convolutional neural networks (CNNs) have outstanding advantages in this field. Deep CNNs (D‐CNNs) with better performance tend to have more parameters and higher complexity. However, shallow CNNs have difficulty extracting the key features of complex remote sensing images. In this paper, we propose a lightweight network with a random depth strategy for remote sensing scene classification (LRSCM). We construct a convolutional feature extraction module, DCAB, which incorporates depthwise separable convolutional and inverted residual structures, effectively reducing the numbers of required parameters and computations, and retains and utilizes low‐level features. In addition, coordinate attention (CA) is integrated into the module, thereby further improving the network's ability to extract key local information. To further reduce the complexity of model training, the residual module adopts a stochastic depth strategy, providing the network with a random depth. Comparative experiments on five public datasets show that the LRSCM network can achieve results comparable to those of other state‐of‐the‐art methods.

Cross-domain scene classification is proposed to address the problems of difficult labeling of remote sensing image datasets and poor generalization ability of supervised models, aiming to better utilize existing knowledge. In order to minimize the difference between the source and target domain distributions, many deep domain adaptation methods have been generated, but most of them are based on the difference metric function, which only globally aligns the edge distributions without taking into account the effect of each sample on the network in different and the relationship between related subdomains in different domains of the same category, resulting in a large amount of fine-grained information being lost. In addition, existing domain adaptation methods do not adaptively balance the weights of the marginal and conditional distributions well. To overcome the above difficulties, a multiscale attention-based subdomain dynamic adaptation (SAMRA) method is proposed. The relative importance of the two is balanced by calculating the dynamic weights of each sample in the different domains to globally adjust the marginal distribution, and by obtaining finer-grained key information from the subdomains to locally adjust the conditional distribution. Moreover, multiscale feature generation and attention mechanisms support the extraction of more robust features and more complete information, as well as more purposeful transfer. The results of cross-domain experiments show that the improvement of SAMRA over state-of-the-art deep-domain adaptation methods is significant.

The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challenging to accurately locate and mine these features, which in turn makes accurate interpretation difficult. In order to address the aforementioned issues, this paper proposes a dynamic convolution covariance network (ODFMN) based on omni-dimensional dynamic convolution (ODConv), which can extract multi-dimensional and multi-scale features from RSIs and perform statistical higher-order representation of feature information. First, in order to fully exploit the complex spatial context information of RSIs and at the same time improve the limitation of a single static convolution kernel for feature extraction, we constructed a omni-dimensional feature extraction module (ODFE) based on dynamic convolution, which fully extracts the four-dimensional information within the convolution kernel. Then, to make full use of the full-dimensional feature information extracted from each level in the network, the feature representation is enriched by constructing multi-scale feature fusion module (MFM) to establish relationships from local to global. Finally, higher-order statistical information is employed to address the challenge of representing first-order information for smaller object features, which is inherently difficult to do. Experiments conducted on publicly available datasets have demonstrated that the method achieves high classification accuracies of 99.04%, 95.34%, and 92.50%, respectively. Furthermore, the method has been verified to have high capture accuracy for feature target contours, shapes, and spatial context information through feature visualisation.

Remote sensing scene images contain numerous feature targets with unrelated semantic information, so how to extract to the local key information and semantic features of the image becomes the key to achieving accurate classification. Existing Convolutional Neural Networks (CNNs) mostly concentrate on the global representation of an image and lose the shallow features. To overcome these issues, this paper proposes LPNet for remote sensing scene image classification. First, LPNet employs LKConv to extract the semantic features in the image, while using standard convolution to extract local key information in the image. Additionally, the LPNet applies a shortcut residual concatenation branch to reuse features. Then, parameter fusion combines parameters from previous branches, improving the capacity of the model to obtain a more comprehensive and rich feature representation of the image. Finally, considering the relationship between the classification ability of the model and the depth of feature extraction, the Feature Mixture (FM) Block is used to deepen the model for feature extraction. Comparative experiments on four publicly available datasets show that LPNet provides comparable results to other state‐of‐the‐art methods. The effectiveness of LPNet is further demonstrated by visualizing the effective receptive fields (ERFs).