In building extraction, collecting pixel-level annotations required by fully supervised methods is extremely costly. Image-level weakly supervised methods based on class activation maps (CAMs) effectively reduce the cost and have shown promising progress. However, generating high-quality CAMs remains challenging due to the supervision gap between classification and segmentation tasks. Specifically, image-level supervision causes CAMs to activate only the most discriminative regions, which compromises the integrity of CAMs. Meanwhile, the absence of pixel-level supervision leads to a depletion of spatial information, resulting in imprecise boundaries. In this study, we propose a novel image-level weakly supervised building extraction method based on view consistency, named ViewCAM, to generate high-quality CAMs. The view transformation module is designed to apply view transformations to remote sensing images and the high-dimensional features. Additionally, a feature affinity enhancement module (FAEM) is proposed to capture positional relationships between pixels and low-level features, such as edges and textures, improving boundary fineness. We integrate these two modules into a classification network and incorporate pixel-level supervision using view-consistency constraints. The entire network is then trained in an end-to-end manner, leading to improved integrity and boundary fineness of the seeds generated from CAMs. To verify the effectiveness and robustness of ViewCAM, we conduct experiments on two representative datasets, and the results demonstrate that our proposed method achieves superior CAM integrity and boundary fineness, outperforming state-of-the-art methods.

Silent high-dynamic target recognition using single receiver has garnered significant attention due to its advantages in safety and stealth within military operations. This paper presents an in-depth exploration of a silent high-dynamic target recognition method based on frequency modulation (FM) signals. The proposed method processes the FM signals with a least squares filtering approach and further implements the calculation of Cross-Ambiguity Function (CAF) mapping to achieve target imaging within the CAF spectrum. By capturing FM signals with an antenna array tuned to various orientations, we conducted empirical analysis using actual aircraft in flight as the target for identification. The recognition scheme put forth by our research is capable of precisely determining the velocity of the target and the relative distance parameters between dual base stations, facilitating accurate tracking of targets.

Falls, a common type of accident, especially among the elderly and those with mobility impairments, potentially leading to serious physical injuries and health issues. Fall detection refers to the use of sensors, monitoring equipment, or other technological means to monitor and identify occurrences of falls. Currently, a lot of research on fall detection from various aspects such as vision, wearable devices, and multi-modal. However, current vision-based fall detection datasets are limited to single indoor scenarios and do not consider outdoor scenarios. Therefore, in this paper we propose a multi-perspective indoor and outdoor scenarios fall detection dataset based on campus surveillance, which include both indoor and outdoor campus scenarios. We employed YOLOv5 to carry out experiments on our proposed dataset as a benchmark. Moreover, we undertook comparative experiments against other datasets and assessed the richness of our dataset. The experiment results reveal that our dataset encompasses more diverse scenarios than other vision-based fall detection dataset.

With the development of deep learning and the increase in the amount of data, general artificial intelligence models have become a popular research area nowadays. When facing a new application scenario, a pretraining general model can often show better performance than models trained with new data on its own. However, because of the specificity of the differences in hyperspectral image data bands, the current hyperspectral image classification (HSIC) field has not proposed a better general model training solution, and it is difficult to utilize the information of the existing hyperspectral datasets for model training in the face of a new scenario. In order to solve this problem, this article proposes a generalized hyperspectral classification model training method, which effectively completes the training of hyperspectral classification models across datasets by adaptive channel module and masked self-supervised pretraining method, and can pretrain and fine-tune hyperspectral classification models using multiple datasets. The adaptive channel module is able to solve the band difference problem of using hyperspectral datasets across datasets, and the masked self-supervised learning method solves the label difference and labeling difficulties of training models across datasets. Experimental results on multiple datasets show that the method proposed in this article can effectively use a large amount of data to complete the pretraining of hyperspectral classification models, and the fine-tuning results on downstream datasets have certain advantages relative to current advanced deep learning methods.

The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability of the method and the ability to extract modulation characteristics. This paper proposes an automated machine learning framework, AutoSMC, for signal modulation classification. An adaptive signal augmentation method is proposed to adapt to the network changes during the search process. In order to extract the modulation features effectively, an scalable convolutional random fourier feature block is proposed. Moreover, the initial search space of the framework is given. The Bayesian Optimization is used to drive hyperparameter optimization to achieve AutoSMC and obtain the optimal method state. Great experiments were carried out on RADIOML 2016.10A and RADIOML 2016.10B. Experimental evaluations on these datasets show that our approach AutoSMC achieves state-of-the-art results compared to the most relevant signal modulation classification methods.

In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recognize object instances using only image-level labels, which greatly saves the labeling costs of RSIs. In this article, we propose a self-directed weakly supervised strategy (SD-WSS) to perform WSOL in RSIs. To specify, we fully exploit and enhance the spatial feature extraction capability of the RSIs' classification model to accurately localize the objects of interest. To alleviate the serious discriminative region problem exhibited by previous WSOL methods, the spatial location information implicit in the classification model is carefully extracted by GradCAM++ to guide the learning procedure. Furthermore, to eliminate the interference from complex backgrounds of RSIs, we design a novel self-directed loss to make the model optimize itself and explicitly tell it where to look. Finally, we review and annotate the existing remote sensing scene classification dataset and create two new WSOL benchmarks in RSIs, named C45V2 and PN2. We conduct extensive experiments to evaluate the proposed method and six mainstream WSOL methods with three backbones on C45V2 and PN2. The results demonstrate that our proposed method achieves better performance when compared with state-of-the-arts.

Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral images information collected from the same area at different time periods. Therefore, researchers are currently eager to construct a HSIC model that continuously acquires new classes of data. During the continuous learning process, the model is expected to not only effective in extracting unique spatial-spectral features of the hyperspectral image, but also ensures the ability to maintain the old classes knowledge while learning new data. To achieve this purpose, we propose a method which based on geometric spatial-spectral feature integration network with class incremental learning (GS2FIN-CIL) framework in continuous learning to make the model adaptable to new classes data and not overly forgetting the old classes knowledge during the training process. We conduct extensive experiments with the proposed GS2FIN-CIL method on widely-used hyperspectral datasets including Indian Pines, PaviaU and Salinas. The experimental results show that our GS2FIN-CIL method can achieve significantly improved results compared to current state-of-the-art class incremental learning methods, allowing for efficient adaptation and utilization of spatial-spectral features in processing new classes of hyperspectral images and alleviating the problem of catastrophic forgetting of learned old classes knowledge. The GS2FIN-CIL method could be successfully applied to the challenge of adding new classes data in HSIC task.

Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large volume of optical remote sensing ship images as the source domain training detection framework, and the second employs SAR ship images and optical remote sensing ship images to create a few-shot balanced subset fine-tuning detection framework. Use a metric learning-based prediction box classifier instead of a fully connected prediction box classifier. When fine-tuning the whole detection frame using the metric learning-based pre-diction frame classifier, the experiments show that an AP50 of 55.99% can be reached with only 10 SAR ship images.