The remote sensing image scene classification continues to face significant challenges due to high intraclass diversity and interclass similarity. Existing methods mainly use semantic associations between images to establish deep semantic associations between classes, ignoring the rich high-level semantic knowledge contained in the label text. This high-level information are especially valuable for distinguishing between confusing categories, as it enables the model to capture both similarity and distinctive features effectively. In this article, we introduce a novel approach that incorporates label semantic information and proposes a plug-and-play framework to guide classification model learning of intraclass and interclass relationships. Specifically, our framework includes a dynamic soft label module (DSLM), which uses textual semantics to facilitate classification model learning of interclass relationships via soft labels at the target level. In addition, we design a coarse-to-fine contrastive module (CFCM) to integrate textual semantics into contrastive learning, guiding the model in capturing intraclass and interclass relationships at the feature level. Our framework is compatible with both convolutional neural network (CNN)-based and vision transformer (ViT)-based classification architectures and is employed solely during training to minimize computational overhead. Experimental results on four datasets validate the effectiveness of our approach.

Vehicle detection in infrared aerial images is vital for both military and civilian applications, as infrared imaging remains effective under low-light conditions and various adverse weather scenarios. However, the longer wavelengths of long-wave infrared, compared to visible light, make diffraction more noticeable, leading to low-frequency degradation of vehicle information. Thermal radiation from the environment and optical system leads to higher noise in infrared images. Additionally, atmospheric transport models for various weather conditions can degrade infrared images to different extents. These factors lead to a reduced signal-to-noise ratio, which complicates the extraction of clear features. To overcome these challenges, we propose the Dual-Tuning Network (DTNet), an advanced framework for vehicle detection in infrared aerial images, developed based on the mechanisms of infrared imaging. Specifically, the core component of DTNet is the Dual-Tuning Block (DTBlock), which works alongside the Dynamic Guided Filtering Module (DGFM) and the Point Spread Recovery Module (PSRM) for feature extraction. DTBlock decomposes feature maps into low- and high-frequency components with learnable low-pass filters. DGFM eliminates disturbance from the optical system and background thermal radiation in the high-frequency component of feature maps, while preserving the details and texture of vehicles. PSRM aggregates vehicle features in the low-frequency component of feature maps, which is proposed with reference to diffraction and atmospheric models. The concept of Dual-Tuning refers to enhancing the signal and suppressing interference in the low and high frequency parts, respectively. Experimental results on the DroneVehicle public dataset for infrared vehicle detection indicate that our proposed approach achieves state-of-the-art (SOTA) performance. Moreover, extensive ablation studies confirm the superior capability of our DTNet in robust feature extraction from infrared images.

For ship targets in down-sight conditions, the aspect ratio is uneven. Therefore, horizontal box labeling can lead to the interference of too much background information, making the target modeling inaccurate. To address this issue, a Rotated Discriminative Scale Space Tracker (roDSST) based on DSST is proposed. Firstly, the sampling method has been refined. The rotated target area is sampled using bilinear interpolation to reduce background interference and avoid quantization errors. Secondly, an angle filter is introduced to predict the rotation angle of the bounding box. Finally, the template update mechanism is improved by always retaining a certain weight of the initial template. The experimental results show that roDSST has superior performance in tracking accuracy and robustness on downward-looking ship sequences.

Infrared sensors are highly valuable in both civil and military applications due to their immunity to weather and time conditions. Among the various targets in infrared remote sensing images, vehicle targets are the most prevalent and significant. However, Existing deep learning-based detection algorithms face challenges such as high false alarm rates and low efficiency when applied to infrared weak vehicle targets detection. To address the aforementioned issues, we propose a Multi-scale Adaptive Feature Enhancement Network, named MAFE-Net, which focuses on highlighting significant features of vehicles and the correlation between targets and their surrounding environments. It consists of two significant designs: the self-adaptive coordinate attention (SCA) for feature enhancement and the multi-scale self-attention module (MSM) for feature fusion. In the meantime, we created the IRSWV dataset, which comprises 4,770 infrared aircraft-captured images. Comparing our algorithm with other current mainstream target detection algorithms on the IRSWV dataset, our algorithm demonstrates significant advantages.

Infrared vehicle detection based on aerial images has significant applications in military and civilian fields for the perception ability under low-light and foggy conditions. However, it remains challenging due to the following characteristics. First, infrared textures and edges are blurred, implying a plenty of low-frequency signals and a shortage of detailed descriptions. Second, objects in infrared images present different patterns depending on their thermal radiations, which hamper the feature extraction of convolution kernels. Third, infrared images lack color information, which means fewer features for classification and regression can be used. Inspired by cognitive neuroscience that humans perceive the entirety from low-frequency information and discern details from high-frequency information, we devise a new framework for oriented infrared vehicle detection called I2MDet (Infrared Information Mining Detector) to tackle the above challenges. It consists of two significant designs: the kaleidoscope module and the semantic feature supplement module (SFSM). In the kaleidoscope module, we explore the effect of kernel sizes and dilation rates on frequency information mining with kaleidoscope-like equivalent kernels. Features in this module are extracted by adaptive involution operators instead of convolution kernels to deal with multiple patterns. The SFSM provides the network with features beneficial for classification and regression. On the one hand, the network is guided to learn more meaningful features under semantic supervision. On the other hand, features output by the SFSM supplement the detection head with semantic information. Experimental results on the public dataset DroneVehicle demonstrate that our proposed approach achieves outstanding performance on oriented infrared vehicle detection.

Aircraft is easily covered by clouds in optical remote sensing images. It is a challenge to detect the aircraft and recognize its sub-categories in this situation. However, the methods proposed by the current research are mainly applied to high-quality images, which do not perform well on cloudy images. In this paper, an adaptive feature aggregation network called AFA-Net is proposed to solve this problem. We design a mixed self-attention module that adaptively focuses on the uncovered parts of the aircraft and its neighborhood from space and channel in feature maps. Experiments were done on the Optical Image Aircraft Detection and Recognition Data Set of the 3rdTianzhibei Challenge. Compared with the most advanced object detection algorithms, the proposed approach achieves state-of-the-art performance.