Airplane detection is a challenging problem in complex remote sensing imaging. In this letter, an effective airplane detection framework called Markov random field-fully convolutional network (M-FCN) is proposed. The M-FCN uses a cascade strategy that consists of an FCN-based coarse candidate extraction stage, a multi-Markov random field (multi-MRF)-based region proposal (RP) generation stage, and a final classification stage. In the first stage, the FCN model is trained to be sensitive to airplanes, and a coarse candidate map is generated. This model is scale-, direction-, and color-invariant and does not require many training examples. After the first stage, the coarse candidate map is used as the initial labeling field for a multi-MRF algorithm, and RPs are generated according to the multi-MRF output. This RP-generating strategy can yield more accurate locations with fewer RPs. In the last stage, a convolutional neural network-based classifier is used to improve the precision of the entire framework. Experiments show that the M-FCN has high precision, recall, and location accuracy.

With the development of remote-sensing technology, optical remote-sensing imagery processing has played an important role in many application fields, such as geological exploration and natural disaster prevention. However, relative radiation correction and geometric correction are key steps in preprocessing because raw image data without preprocessing will cause poor performance during application. Traditionally, remote-sensing data are downlinked to the ground station, preprocessed, and distributed to users. This process generates long delays, which is a major bottleneck in real-time applications for remote-sensing data. Therefore, on-board, real-time image preprocessing is greatly desired. In this paper, a real-time processing architecture for on-board imagery preprocessing is proposed. First, a hierarchical optimization and mapping method is proposed to realize the preprocessing algorithm in a hardware structure, which can effectively reduce the computation burden of on-board processing. Second, a co-processing system using a field-programmable gate array (FPGA) and a digital signal processor (DSP; altogether, FPGA-DSP) based on optimization is designed to realize real-time preprocessing. The experimental results demonstrate the potential application of our system to an on-board processor, for which resources and power consumption are limited.

Recently, ship detection in synthetic aperture radar (SAR) images has received extensive attention. Most of the current ship detectors preset dense anchor boxes to achieve spatial alignment with ground-truth (GT) objects. Then, the detector defines the positive and negative samples based on the intersection-over-unit (IoU) between the anchors and GT objects. However, this label assignment strategy confuses the learning process of the model to a certain extent and results in suboptimal classification and regression results. In this paper, an adaptive sample assignment (ASA) strategy is proposed to select high-quality positive samples according to the spatial alignment and the knowledge learned from the regression and classification branches. Using our model, the selection of positive and negative samples is more explicit, which achieves better detection performance. A regression guided loss is proposed to further lead the detector to select well-classified and well-regressed anchors as high-quality positive samples by introducing the regression performance as a soft label in the calculation of the classification loss. In order to alleviate false alarms, a feature aggregation enhancement pyramid network (FAEPN) is proposed to enhance multi-scale feature representations and suppress the interference of background noise. Extensive experiments using the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) demonstrate the superiority of our proposed approach.

Recently, remote sensing image scene classification technology has been widely applied in many applicable industries. As a result, several remote sensing image scene classification frameworks have been proposed; in particular, those based on deep convolutional neural networks have received considerable attention. However, most of these methods have performance limitations when analyzing images with large intraclass variations. To overcome this limitation, this letter presents the marginal center loss with an adaptive margin. The marginal center loss separates hard samples and enhances the contributions of hard samples to minimize the variations in features of the same class. Experimental results on public remote sensing image scene data sets demonstrate the effectiveness of our method. After the model is trained using the marginal center loss, the variations in the features of the same class are reduced. Furthermore, a comparison with state-of-the-art methods proves that our model has competitive performance in the field of remote sensing image scene classification.

At present, the raw echo data captured by spaceborne SAR is usually downlinked to the ground stations for imaging and analysis. If the SAR data can be processed on-board, the processing results could be transmitted to users directly through broadcast distribution, which can greatly reduce the delay time for emergency applications. However, on-board processing implementation faces many challenges due to the space radiation environment and limited resources of the satellite. In this paper, an FPGA-based implementation of ship detection for on-board processing is proposed, which has high efficiency in logic and memory resources. First, the hardware-oriented clustering approach is proposed. Image processing operations with similar regularity of data access can be mapped in one processing engine, which can reduce the employment of logic resources. Second, the concept of the data-buffering cycle (DBC) is proposed. The DBC provides a method to analyze the intermediate data and optimize the memory reuse. Finally, based on the above optimization methods, the FPGA-based implementation for a ship detection algorithm is presented. Compared with the traditional methods, the experimental results show the efficacy of our proposed method with lower consumption in logic and memory resources.

Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.