Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

In deep learning-based hyperspectral remote sensing image classification tasks, random sampling strategies are typically used to train model parameters for testing and evaluation. However, this approach leads to strong spatial autocorrelation between the training set samples and the surrounding test set samples, and some unlabeled test set data directly participate in the training of the network. This leaked information makes the model overly optimistic. Models trained under these conditions tend to overfit to a single dataset, which limits the range of practical applications. This paper analyzes the causes and effects of information leakage and summarizes the methods from existing models to mitigate the effects of information leakage. Specifically, this paper states the main issues in this area, where the issue of information leakage is addressed in detail. Second, some algorithms and related models used to mitigate information leakage are categorized, including reducing the number of training samples, using spatially disjoint sampling strategies, few-shot learning, and unsupervised learning. These models and methods are classified according to the sample-related phase and the feature extraction phase. Finally, several representative hyperspectral image classification models experiments are conducted on the common datasets and their effectiveness in mitigating information leakage is analyzed.

The complementary characteristics of SAR and optical images are beneficial in improving the accuracy of land cover classification. Deep learning-based models have achieved some notable results. However, how to effectively extract and fuse the unique features of multi-modal images for pixel-level classification remains challenging. In this article, a two-branch supervised semantic segmentation framework without any pretrained backbone is proposed. Specifically, a novel symmetric attention module is designed with improved strip pooling. The multiple long receptive fields can better perceive irregular objects and obtain more anisotropic contextual information. Meanwhile, to solve the semantic absence and inconsistency of different modalities, we construct a multi-scale fusion module, which is composed of atrous spatial pyramid pooling, varisized convolutions and skip connections. A joint loss function is introduced to constrain the backpropagation and reduce the impact of class imbalance. Validation experiments were implemented on the DFC2020 and WHU-OPT-SAR datasets. The proposed model achieved the best quantitative values on the metrics of OA, Kappa and mIoU, and its class accuracy was also excellent. It is worth mentioning that the number of parameters and the computational complexity of the method are relatively low. The adaptability of the model was verified on RGB–thermal segmentation task.

Object detection plays a crucial role in remote sensing due to the urgent demands of various applications, such as urban planning and environmental monitoring. Despite notable progress, current methods still struggle with detecting challenging small objects. At the object level, the limited pixel representation, blurred details, and background interference of small objects impose greater demands on feature extractors. At the network level, resource bias fails to provide adequate learning signals for these objects. In this paper, we propose a Sparse Transformer-based detector (STDet) to tackle these challenges. Specifically, we design a Local-to-Global Transformer network (LGFormer) to explore essential feature representations. The Local Transformer Block establishes correlations between tokens and their surrounding data, while the Global Transformer Block captures long-distance dependencies related to the objects. Meanwhile, we introduce a Scale-Balanced Label Assignment (SBLA) strategy that considers more samples to small objects. SBLA dynamically shifts the learning focus to easily overlooked objects and alleviates the issue of sample imbalance. Extensive experiments on three large-scale remote sensing datasets demonstrate the effectiveness of STDet and its superiority in small object detection.

Deep learning has made significant achievements in remote sensing object detection tasks. However, small weak objects located in complex scenes are still not effectively addressed. The lack of feature information and negligible contributions during the optimization stage are the main reasons. To solve the indicated issues, a novel remote-sensing object detection method is proposed in this letter. Firstly, the context feature integration module (CFIM) is designed to extract implicit clues co-occurring with the object to compensate for the lack of features in small weak objects. The receptive field expansive deformable convolution (RFConv) constructed in CFIM can adaptively adjust the information extraction range based on the object’s characteristics, thereby capturing suitable context features. Secondly, to make small weak objects competitive during the optimization process, we propose tailored optimization functions: the balanced sampling strategy (BSS) and the modulated loss function (MLF). BSS makes up for the sample deficiency of small weak objects by balancing sampling. More specifically, BSS dynamically mines potential positive samples from the ignored set in order to increase the chances of matching. MLF progressively adjusts the loss proportion of the object and pushes the detector to be more sensitive to small weak objects during the training stage. Our proposed method achieves 94.79% and 71.23% mAP on the NWPU VHR-10 and DIOR datasets, which also proves the effectiveness.