In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

Recent advances in camera-equipped drone applications increased the demand for visual object detection algorithms with deep learning for aerial images. There are several limitations in accuracy for a single deep learning model. Inspired by ensemble learning can significantly improve the generalization ability of the model in the machine learning field, we introduce a novel integration strategy to combine the inference results of two different methods without non-maximum suppression. In this paper, a global and local ensemble network (GLE-Net) was proposed to increase the quality of predictions by considering the global weights for different models and adjusting the local weights for bounding boxes. Specifically, the global module assigns different weights to models. In the local module, we group the bounding boxes that corresponding to the same object as a cluster. Each cluster generates a final predict box and assigns the highest score in the cluster as the score of the final predict box. Experiments on benchmarks VisDrone2019 show promising performance of GLE-Net compared with the baseline network.

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

Gaze estimation is a cornerstone of applications such as human–computer interaction and behavioral analysis, e.g., for intelligent transport systems. Nevertheless, existing methods predominantly rely on coarse-grained features from deep layers of visual encoders, overlooking the critical role that fine-grained details from shallow layers play in gaze estimation. To address this gap, we propose a novel Hierarchical Fine-Grained Attention Decoder (HFGAD), a lightweight fine-grained decoder that emphasizes the importance of shallow-layer information in gaze estimation. Specifically, HFGAD integrates a fine-grained amplifier MSCSA that employs multi-scale spatial-channel attention to direct focus toward gaze-relevant regions, and also incorporates a shallow-to-deep fusion module SFM to facilitate interaction between coarse-grained and fine-grained information. Extensive experiments on three benchmark datasets demonstrate the superiority of HFGAD over existing methods, achieving a remarkable 1.13° improvement in gaze estimation accuracy for in-car scenarios.