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.

In recent years, with the extensive application of deep learning in images, the task of remote sensing image change detection has witnessed a significant improvement. Several excellent methods based on Convolutional Neural Networks and emerging transformer-based methods have achieved impressive accuracy. However, Convolutional Neural Network-based approaches have difficulties in capturing long-range dependencies because of their natural limitations in effective receptive field acquisition unless deeper networks are employed, introducing other drawbacks such as an increased number of parameters and loss of shallow information. The transformer-based methods can effectively learn the relationship between different regions, but the computation is inefficient. Thus, in this paper, a multi-scale feature aggregation via transformer (MFATNet) is proposed for remote sensing image change detection. To obtain a more accurate change map after learning the intra-relationships of feature maps at different scales through the transformer, MFATNet aggregates the multi-scale features. Moreover, the Spatial Semantic Tokenizer (SST) is introduced to obtain refined semantic tokens before feeding into the transformer structure to make it focused on learning more crucial pixel relationships. To fuse low-level features (more fine-grained localization information) and high-level features (more accurate semantic information), and to alleviate the localization and semantic gap between high and low features, the Intra- and Inter-class Channel Attention Module (IICAM) are integrated to further determine more convincing change maps. Extensive experiments are conducted on LEVIR-CD, WHU-CD, and DSIFN-CD datasets. Intersection over union (IoU) of 82.42 and F1 score of 90.36, intersection over union (IoU) of 79.08 and F1 score of 88.31, intersection over union (IoU) of 77.98 and F1 score of 87.62, respectively, are achieved. The experimental results achieved promising performance compared to certain previous state-of-the-art change detection methods.

Highly pathogenic avian influenza H5N1 remains a serious concern for both poultry and human health. Wild waterfowl are considered to be the reservoir for low pathogenic avian influenza viruses; however, relatively little is known about their movement ecology in regions where HPAI H5N1 outbreaks regularly occur. We studied movements of the ruddy shelduck (Tadorna ferruginea), a wild migratory waterfowl species that was infected in the 2005 Qinghai Lake outbreak. We defined their migration with Brownian Bridge utilization distribution models and their breeding and wintering grounds with fixed kernel home ranges. We correlated their movements with HPAI H5N1 outbreaks, poultry density, land cover, and latitude in the Central Asian Flyway. Our Akaike Information Criterion analysis indicated that outbreaks were correlated with land cover, latitude, and poultry density. Although shelduck movements were included in the top two models, they were not a top parameter selected in AICc stepwise regression results. However, timing of outbreaks suggested that outbreaks in the flyway began during the winter in poultry with spillover to wild birds during the spring migration. Thus, studies of the movement ecology of wild birds in areas with persistent HPAI H5N1 outbreaks may contribute to understanding their role in transmission of this disease.

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data.

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.