Semantic segmentation is a key means for understanding very-high resolution (VHR) aerial imagery. With the explosive development of deep learning, deep learning methods are being applied to the segmentation of VHR images, with convolutional neural networks (CNNs) as the basic framework. However, owing to the highly complex details present in VHR images and the high spatial dependence of geographical objects, CNN-based methods are inadequate. This is because the inherent locality of CNNs limits the size of the receptive field, thus limiting the ability to obtain long-range context information. To solve this problem, in this paper, we propose a transformer-based novel deep learning model called crisscross-global vision transformers (CGVT). CGVT exploits the transformer’s inherent ability to obtain long-range context information to solve the restricted receptive field problem. Specifically, we redesign the self-attention mechanism in the transformer and call it crisscross-global attention. It consists of two parts: crisscross transformer encoder block (CC-TEB) and global squeeze transformer encoder block (GS-TEB). CC-TEB overcomes the limitation of the traditional self-attention design (specifically, difficulty applying it to VHR aerial image segmentation) and further increases the local feature representation ability of the model. GS-TEB increases the global feature representation ability of the model. The results of experiments conducted on the popular ISPRS Vaihingen, IEEE GRSS Data Fusion Contest Zeebrugge, and LoveDA Semantic Segmentation Challenge datasets verify the effectiveness and superiority of our proposed method. Specifically, it achieved state-of-the-art performance on both Zeebrugge and LoveDA datasets, and is currently ranked second in Vaihingen dataset.

Remote sensing images, especially hyperspectral images (HSIs), are extremely vulnerable to random noise and stripe noise. As a key aspect of HSI data quality improvement, stripe noise removal has always been a pervasive issue in remote sensing image processing. Convolutional neural networks have been applied for HSI data destriping. However, the existing methods lose the stripe-free component of the original image to a certain extent. These models also ignore the global spatial context of images and the correlation between spatial information and spectral information. Therefore, we propose a novel destriping convolutional network to overcome the problems with the existing methods. Octave convolution is used to extract cross-frequency features, and separate and compress the low-frequency information of the images, while dilation convolution (Dila-Conv) is used to reduce the amount of required calculation and also preserve the key image information. In addition, Dila-Conv can expand the receptive field to obtain multiscale features. Finally, a cross-channel enhanced spatial–spectral feature fusion module is used to acquire and integrate spatial context information and interchannel dependencies on a global scale as auxiliary information so that the network model can learn and pay attention to key feature information, specifically, “what to look for” and “where to look at,” which can facilitate the distinction between stripe and stripe-free components. Experimental results obtained using multiple datasets demonstrated that the proposed method can outperform the existing comparable methods and can produce satisfactory results in terms of visual effects and quantitative evaluation.

In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.

Unmanned Aerial Vehicle (UAV) small object detection remains challenging due to extreme scale variation, dense spatial distribution, and frequent occlusion in aerial images. Small objects often occupy very limited pixel regions and exhibit ambiguous structural boundaries, making their discriminative features easily overwhelmed by background clutter. These challenges lead to unstable multi scale representation and degraded localization precision in existing detectors. To address these challenges, combining Mamba and YOLO, we propose the HEdge-MamYOLO, which consists of three components. First, we design Frequency-Mamba Collaborative High-Frequency Enhancement Module (FM-CHFEM) to extract high-frequency components of enhanced visible edges from occluded objects in the frequency domain and leverage Mamba global selective scanning combined with spatial similarity to unoccluded objects of the same type to achieve feature enhancement and recognition of occluded objects. Second, we propose the Dynamic Scale Feature Fusion Module (DSFFM), by covering large-scale objects and adaptively matching small-scale object features, achieves balanced capture of objects at different scales under FM-CHFEM-based feature extraction. Finally, we design the Lightweight Low-Level Feature Fusion Head (LLFFH) by integrating high-resolution low-level features with DSFFM’s scale-adaptive features, it sparsely processes feature maps via partial convolution. This approach preserves details of small objects, and reduces computational overhead. Extensive experiments on the VisDrone2019 and UAVDT aerial image datasets, with mAP50 metrics reaching 52.5%and 33.4% respectively, which is better than other SOTA models, demonstrate the effectiveness of our proposed method.