Infrared remote-sensing images have irreplaceable value in military and civilian research, such as remote surveillance and military reconnaissance. However, under the conditions of complex scenes, infrared ship detection still faces great challenges. Most importantly, because of the limited hardware resource, the spaceborne satellites usually have weak computational processing ability, which makes the traditional convolution neural network (CNN)-based detection algorithms difficult to show their power. In terms of the above facts, this article proposes a high-performance but low-computation and storage-efficient ship detection algorithm to adapt the severe spaceborne environment. Overall, our method contains the following technical steps: 1) we first present a novel iterative precise segmentation of land and sea algorithm to preprocess the complex and diverse remote-sensing scenes; 2) the multivariate Gaussian distribution is then selected to extract the ship target candidate regions to guarantee the detection recall; 3) we adopt the optical panchromatic data to assist the limited infrared data training; and 4) the cascade decision of multisource features including global and local cues is next utilized to gradually eliminate false alarms. Due to the high efficiency, the proposed method can implement well on the hardware platform of DSP and field-programmable gate array (FPGA) architecture. We conduct a series of experiments and compare with the state-of-the-art object detection algorithms. Experimental results show that our method has fewer parameters but can achieve strong detection robustness against the noise, cloud and reef interfere, which verifies the effectiveness of the proposed method.

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.