Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task loss function is used to jointly supervise model training to improve the performance of the network. Extensive experimental results on the challenging SECOND and Landsat-SCD datasets, demonstrate that our SMNet obtains 71.95% and 85.65% at mean Intersection over Union (mIoU), respectively. In addition, the proposed SMNet achieves 20.29% and 51.14% at Separated Kappa coefficient (Sek) on the SECOND and Landsat-SCD datasets, respectively. All of the above proves the effectiveness and superiority of the proposed method.

Remote sensing image scene classification is one of the important means for remote sensing image understanding. Convolutional neural networks have been successfully applied to remote sensing image scene classifications and have demonstrated remarkable performance. However, with improvements in image resolution, remote sensing image categories are becoming increasingly diverse, and problems such as high intraclass diversity and high interclass similarity have arisen. The performance of ordinary convolutional neural networks at distinguishing increasingly complex remote sensing images is still limited. Therefore, we propose a feature fusion framework based on hierarchical attention and bilinear pooling called HABFNet for scene classification of remote sensing images. First, the deep convolutional neural network ResNet50 is used to extract the deep features from different layers of the image, and these features are fused to boost the robustness and effectiveness of the features. Second, we design an improved channel attention scheme to enhance the features from different layers. Finally, the enhanced features are cross-layer bilinearly pooled and fused, and the fused features are used for classification. Extensive experiments were conducted on three publicly available remote sensing image benchmarks. Comparisons with the state-of-the-art methods demonstrated that the proposed HABFNet achieved competitive classification performance.

Point cloud classification of airborne light detection and ranging (LiDAR) data is essential to extract geoinformation. Although deep learning provides a new approach for classification, the time-consuming training process and data dependence prevent its widespread application to point clouds. To solve these problems and leverage the potential of high-performing neural networks, we propose an airborne LiDAR point cloud classification method based on transfer learning. A strategy to generate feature images considering the point cloud spatial distribution is first introduced for applying traditional convolutional neural networks to point clouds. Then, transfer learning is used to extract multiscale and multiview deep features. A simple neural network classifier is designed to reduce dimensionality, fuse and learn high-level features, and postprocessing considering contextual information further improves the classification accuracy. We verified the performance of the proposed method through experiments on two airborne LiDAR data sets with different characteristics and containing eight classes. The results demonstrate that the proposed method can achieve a satisfactory classification accuracy with relatively short training time and less training samples than if using conventional methods.

Characterized by complicated backgrounds, various types, large size variations, and arbitrary orientations, the detection and recognition of arbitrary-oriented objects in remote sensing images are challenging. To address the aforementioned problem, an anchor-free arbitrary-oriented object detector using box boundary-aware vectors is proposed. With the idea of CenterNet to detect objects as points, oriented object detection is achieved by predicting the center, the box boundary-aware vectors, the size, and the type of the bounding box. In the feature extraction stage of the designed architecture, Res2Net, a multiscale convolutional neural network, is used to extract feature maps of different scales and adaptively spatial feature fusion is adopted to improve the detector's adaptability to objects of different sizes. In the detector, a context enhancement module with a multibranch network is designed to enhance the contextual information of the objects and improve the detector's robustness to the complicated backgrounds. Experiments are carried on three challenging benchmarks (i.e., HRSC2016, UCAS-AOD, and DOTA) and our method achieves state-of-the-art performance with 90.30%, 89.70%, and 77.18% mAP, respectively.

The detection and recognition of oriented objects in remote sensing images is a challenging task due to their complex backgrounds, various sizes, diverse aspect ratios, and especially arbitrary orientations. Many oriented object detection algorithms need to obtain accurate angles or adopt anchors to predict the oriented bounding boxes. When directly predicting the angles of objects’ oriented bounding boxes, the loss of angle is discontinuous during training, which makes it difficult to obtain accurate boundary of oriented objects. And the anchors also aggravate the problems of class imbalance and computational cost. To address the above problems, this paper proposes an anchor-free and angle-free detector called AF ² Det. AF ² Det adopts the information of the bounding box projection instead of the angle to represent and reconstruct the object’s oriented bounding boxes, which could avoid the problem of boundary discontinuity. To predict the information of the bounding box projection, an anchor-free architecture is built to predict objects as points based on a simple but strong U-shaped architecture. And the deformable convolution and the bottom-up feature fusion method are integrated effectively to enhance AF ² Det’ s capacity for objects’ shapes, orientations, and scales. The extensive experiments are conducted on multiple datasets, i.e., HRSC2016, FGSD2021, DOTA, and RSDD-SAR to validate the effectiveness of our method. The experimental results demonstrate that the proposed AF ² Det outperforms other anchor-free algorithms and obtains competitive results on oriented object detection.