In practical applications, the variations in imaging conditions along with changes in ground object states cause spectral shifts within the same class across different domains of hyperspectral images (HSIs), resulting in substantial domain distribution discrepancies. Additionally, the annotation process for HSIs is time-consuming, yielding an insufficient amount of labeled data relative to the needs of strong models, making them prone to overfitting during training. To address these issues, the shift-reduced sample expansion domain generalization network (SSEDGnet) is proposed. Sample diversity is first enhanced by generating expanded domain (ED) samples. Then, feature extraction is jointly performed on multiple source-domain (SD) samples and ED samples to learn domain-invariant representations, which enhances adaptability to unseen target domains (TDs). Specifically, by modeling the full imaging process from stimulation to response, including signal transmission and ground object reflection, the ground object reflection is separately extracted and used to directly generate ED samples through stimulation, thereby obtaining samples with reduced domain shift. Subsequently, feature extraction and fusion at different levels are carried out on both the SDs and EDs. Finally, the classifier conducts the classification. The experimental results on four public HSI datasets show that the proposed method effectively learns a model with superior generalization ability and stability, outperforming state-of-the-art methods. The code will be released soon on the site ofhttps://github.com/Cherrieqi/SSEDGnet

Detecting tiny objects in remote sensing images has been an intriguing yet challenging topic in the remote sensing image processing. While significant progress has been made in many studies, most existing methods focus on improving the accuracy of tiny object detection without particular consideration for computational complexity, which restricts their applicability in resource-limited condi-tions. Therefore, this paper aims to design a lightweight de-tection algorithm tailored for tiny objects in remote sensing images. First, we investigate the impact of the complexity of different components in deep learning-based object detec-tion models on the accuracy of tiny object detection, includ-ing the backbone and detection head. Then, a dedicated backbone for tiny object detection is proposed, achieving competitive detection accuracy while remaining lightweight. Moreover, we propose a lightweight detection head that in-corporates deformable convolution and optimize the chan-nel dimension. Finally, we combine the above methods to introduce a lightweight network, LTDNet, for tiny object detection in remote sensing images. Benefiting from the dedicated designs for the backbone and detection head spe-cifically for tiny objects, the proposed method can achieve competitive detection accuracy with very low parameters and computational complexity. Extensive experiments are conducted on the AI-TODv2 and LEVIR-Ship datasets, and the results demonstrate the effectiveness of our proposed method. Specifically, the proposed method achieves 54.6% AP50 on the AI-TODv2 dataset with only 4.85M parameters and 38.19G FLOPs. The code will be released soon on the site of https://github.com/dyl96/LTDNet.

Remote sensing image change detection has important applications in many fields. However, current studies mostly focus on identifying pixel-level changes. Although these methods can achieve better performance, this paradigm fails to determine changes in specific object instances due to the definition of the task itself. For this reason, we conduct preliminary exploration and propose a method named OBJ-CD, which can detect the changes of object instance. Specifically, OBJ-CD initially employs a lightweight Siamese object detector to detect objects within two temporal images. Subsequently, OBJ-CD calculates the metric matrix for the detected objects in these images. Finally, the conditions of the object instance change are limited by a certain threshold, and the final object-level change detection results can be obtained. We conducted several experiments on our constructed dataset, and the experimental results indicate that the proposed method can achieve object-level change detection with good performance.

With the development of deep learning, automated road detection has gradually become a popular topic in remote sensing image processing. However, existing road detection methods are still insufficient in occlusion problem, resulting in the occluded road being difficult to be detected and thus broken. In order to solve this problem, we propose a multi-resolution codec road detection network (MCRDnet). MCRDnet consists of a backbone with a codec structure, an edge extraction branch (EEB) and an information supplement module (ISM). The backbone is used to extract the features of the road. In addition, EEB is responsible for extracting edge information and interacting with the backbone to smooth the road edge regions. Moreover, ISM also adopts the codec structure but with the input of the images after 2-fold and 4-fold downsampling. The decoder of ISM plays the roles of image restoration and feature mapping at the same time, fusing features into the backbone to supplement the missing information affected by occlusion. The experimental results on the RNBD dataset demonstrate the effectiveness of the proposed method for mitigating the breakage phenomenon in road detection results.

In the field of remote sensing, the detection of tiny objects has always been an interesting and highly regarded issue. Although many researchers have dedicated their efforts to studying this problem, it still presents numerous challenges due to the complexity of the environment in which tiny objects are presented in remote sensing images. To this end, we propose a remote sensing image tiny objects detection method based on explicit semantic guidance, with a specific focus on regions containing tiny objects. Specifically, we incorporate supervision of the tiny object regions during the training process. This supervision allowed us to extract tiny object regions, thereby forming an explicit attention map. This explicit attention map is employed to semantically modulate the feature map for detecting tiny objects, thus enhancing the regions containing tiny objects while suppressing the background. Extensive experiments are conducted on the AI-TODv2 dataset and the proposed method can achieve an AP of 24.6%. The experimental results demonstrate the effectiveness of the proposed tiny object detection method based on explicit semantic guidance. The code will be released soon on the site of https://github.com/dyl96/ESG_TODNet.

In practical applications, due to the high cost and difficulty of hyperspectral image (HSI) annotation, labels for the target domain (TD) may be either unavailable or insufficient in quantity. To address this issue, we propose a multi-source domain generalization two-branch network (MDGTnet) and train the model only using source domain (SD) HSIs with contrastive learning to classify an unknown TD image. MDGTnet consists of a classifier and two branches, which are intra-domain uniqueness extraction branch (intra-DUEB) and inter-domain commonality extraction branch (inter-DCEB). The intra-DUEB is responsible for mining internal attributes of each SD, which can be seen as imaging environmental characteristics. And the inter-DCEB is applied to extract generic features among different SDs. The features extracted by two branches are fused at different levels respectively to remove the influence of different imaging environments for discriminative class features. We have conducted extensive experiments on four public HSI datasets. The results show that the proposed method outperforms state-of-the-art methods. It can learn robust models and extract highly discriminative features, leading to excellent performance in HSI cross-domain classification. Especially on the Pavia Center dataset, the overall accuracy (OA) is 2.47% higher and kappa coefficient is 2.92% higher than the best results of the other methods. The code will be released soon on the site of https://github.com/Cherrieqi/MDGTnet.

Tiny object detection in the field of remote sensing has always been a challenging and interesting topic. Despite many researchers have been working on this problem, it has not been well solved due to its complexity. In this paper, we analyze the reasons for the poor performance of deep learning-based object detection methods for tiny objects in remote sensing images. Moreover, we propose a new remote sensing image tiny object detection network based on object reconstruction and multiple receptive field adaptive feature enhancement module (MRFAFEM), called ORFENet. Detailedly, object reconstruction aims to reduce the information loss of tiny objects within deep neural networks, which is only used in the training phase and can be discarded in the inference phase. MRFAFEM is designed to enhance the features for detecting tiny objects by dynamically adjusting the multiple receptive field features. We have conducted several experiments on the AI-TODv2 and LEVIR-Ship datasets, both of which are proposed for tiny object detection in remote sensing images. The experimental results indicate the effectiveness of the proposed method. Specifically, the proposed ORFENet can achieve the AP of 24.8% on the AI-TODv2 dataset and 83.3% AP50 on the LEVIR-Ship dataset. The code will be released at https://github.com/dyl96/ORFENet.

The development of deep learning technology provides an especially practical tool for hyperspectral image classification. However, the acquisition of labeled samples in a specific domain is usually time-consuming, which is not conducive to the training of neural network. In addition, different domains bring the phenomenon of ‘same object but different spectrum’ to hyperspectral images, which makes it difficult to directly learn from the available samples of other domains to serve a specific domain. To address this problem, we propose a deep domain adaptation network by aligning the second-order moment of source and target domain through cross-scene transfer learning. Specifically, we use abundant labeled samples in the source domain to train a 3DCNN with the purpose of identifying the target domain. Meanwhile, to reduce the distribution difference, we minimize the covariance distance between source domain and the target domain. The experimental results on two groups of hyperspectral images have shown that the proposed method can outperform several baseline methods.

Lightweight object detection and recognition models are extremely crucial for in-orbit applications, which is the most critical factor for whether deep learning-based object detection and recognition algorithms can be applied to remote sensing satellites for real-time or near real-time processing. Global information is extremely important for object detection and recognition of remote sensing images. However, due to the high computational cost, the existing CNN-based lightweight models over-emphasize on the extraction of local information, while ignoring the global information. For this reason, we propose a lightweight object detection and recognition model (Lightweight Global-Local Detection, LGLDet) based on the especially light global modeling structure. In LGLDet, light global-local module (LGLM) is proposed to extract the global and local information. The LGLM consists of Point2Patch Non-Local (P2PNL), local branch and skip connection. Specifically, P2PNL is proposed to reduce the computation of global long-range dependency modeling. In addition, the feature fusion part and detection head are also designed in a lightweight way. In the experiments, the proposed method can achieve optimal performance with fewer parameters and lower computational complexity than existing CNN-based lightweight models and transformer-based lightweight models with similar parameters or computational complexity. The code will be released on the site of https://github.com/dyl96/LGLDet.

Automatic road detection from remote sensing images has always been a significant research topic. It is of great value to many practical applications. However, there are still some problems need to be solved. First of all, most of existing road detection methods are inefficient because of the sequential processing of the decoder head. Secondly, some existing methods are unable to detect occluded road areas effectively. For this reason, we focus on the speed and occlusion problems in road detection network, and propose a new lightweight road detection method based on multi-scale convolution attention network (MSCAN) and coupled decoder head, LRDNet. In particular, LRDNet adopt multi-scale convolution attention network with large receptive field for feature extraction to solve the occlusion problem, and decode the road surface, road edge and road centerline in a coupled way to improve the speed of road detection and ensure that the road surface detection results have fewer burrs at the road edge. We have performed several experiments on the RNBD dataset. Compared with some state-of-the-art methods, the experimental results prove the validity of the proposed LRDNet. The code will be released soon on the site of https://github.com/dyl96/LRDNet.