## 4 Experimental Results and Discussion
### Overall Detection Performances
(**d**) Detection results of the port. (**e**) Detection results of wave interference. (**f**) Detection results of massive cloud occlusion. LBP part of a ship. In [13], the saliency map based on the multi-scale and multi-direction wavelet decomposition was detected to extract ROIs, and the pixel distribution discrimination was designed to eliminate false alarms. Compared with the two previous methods, this method showed a great improvement in accuracy and the false alarm rate. However, this method was designed for color images, and its performance is degraded when applied to panchromatic images. Moreover, the pixel distribution can only remove targets that vary widely in shape. Compared with other algorithms, the performance of our method is a great improvement. In order to improve recall and accuracy, in the extraction of the ROI stage, the MPQFT was designed for panchromatic remote sensing images. The quaternion was constructed with the full consideration of image contrast and edge structure. In addition, in order to find candidates in complex backgrounds, texture and spatial similarity were also adopted. In the latter stage, we proposed the MLBP method combined with 10 other features to reduce false alarms. Figure 14 shows our detection results in some typical sea conditions. On the whole, the algorithm achieved ideal detection results and detected ship targets under complex sea conditions. At the same time, if the sea surface is calm, our method can basically ensure that false alarms and missed detections are less than 5%. Using the method proposed by this paper, color images should be transformed into gray-scale images for processing. We tested our approach on different sea surfaces in color images. These color images were synthesized from the three spectrums (RGB) from the satellite GF-2 and had an 8 m resolution. Some detection results for color images are shown in Figure 16. The first row contains color images. The second row contains the corresponding gray-scale images and detection results. On the whole, a good performance was achieved. The red border is the target detected by the method. However, it is generally said that the resolution of color images for remote sensing multi-spectral images is much lower than that of panchromatic images. Therefore, the accuracy of this method will be reduced for small ships. As shown in Figure 16, the yellow border is the lost target because the ship is too small in the image with an 8 m resolution. Figure 16: Detection results of color images. (**a**) Color images. (**b**) The results on the corresponding grayscale images. A platform of FPGA combined with DSP was built, and the method of ship detection proposed in this paper was optimized and embedded into a hardware platform. DSP used a TMS320C6678 with an 8-core processor. FPGA adopted the VIRTEX-7 chip produced by the company Xilinx. We carried out a large number of hardware tests of large-field optical remote sensing images. The image size was 8192 \(\times\) 4096 pixels -2 m resolution. The processing time of one image was 242 ms on average. Compared with the performance of the original software method, the efficiency of the method was greatly improved, and the system has strong versatility and expansibility, which lays the foundation for practical engineering applications.