The Visible Infrared Imaging Radiometer (VIIRS) day/night band (DNB) onboard Suomi National Polar-orbiting Partnership (NPP) satellite offers a wide range of applications at night, ranging from fire detection, meteorological phenomena to observations of anthropogenic light sources. It is becoming a useful tool to monitor and quantify these ships by detecting the light emitted by the lamps. In this study, a threshold-based method is presented to automatically identify the ships. Before detection, several pre-processing steps including contrast enhancement and instrument noise removal are conducted; then the background value is subtracted from the original image to reduce the blurred area around the target, which can further make the gathered ships isolated; In addition, the effects of some interference sources such as ionospheric energetic particles and thin clouds are also taken into consideration for improving the detection rate. Finally, the proposed threshold-based method is applied to the DNB images over study areas in Yellow Sea and Bohai Sea in China. The detection results show that the proposed method can detect more than 81% of ships when comparing with those from Automatic Identification System (AIS).
Based on the objects’ sparse features, the compressive sensing imaging system has the unique advantage of breaking the Nyquist sampling theorem, and the target image can be reconstructed from very few random coded observations. The system is characterized by simple coding and complex decoding. It is difficult to meet the increasing real-time requirements in application because of the large time consumption by the iterative optimization algorithms. Therefore, it is a powerful way to improve the efficiency by bypassing the complex reconstruction process and extracting the target information directly from the random measurement data. In this paper, based on MNIST handwritten digital character database as an example, the object recognition method from random measurements of compressive sensing camera is explored. Firstly, the training samples in the MNIST database are coded with the observation of the random Bernoulli measurement matrix. And then the K-nearest neighbor classifier is constructed on the standardized samples, the measurements in the same measurement matrix of the target sample are put in the classifier, given the target recognition results. The experimental results show that the average recognition rate is 82.8% under the sampling rate of 0.1, and the total time to process 500 images is 0.063s. In contrast, the experiment of the traditional method by first reconstructing and then recognizing is conducted, the average recognition rate is 84.3%，and the total time to process 500 images is 48.2s. The proposed method is close to the traditional strategy in recognition accuracy, but the computational efficiency has been greatly improved (765 times), with great practical value.
The compressive sensing imaging technique, based on the realization of random measurement via active or passive
devices (e.g., DMD), has attracted more and more attention. For imaging target of interest within large uniform scene
(e.g., ships in the sea), high-resolution image was usually reconstructed and then used to detect targets, however the
process is time-consuming, and moreover only part of the image consists of the targets of interest. In this paper, the
stepwise multi-resolution fast target detection and imaging method through the combination of different numbers of
DMD mirrors was explored. Low resolution image for larger area target searching and successively higher resolution
image for smaller area containing the targets were reconstructed. Also, non-imaging fast target detection was realized
based on the detector energy intensity, which includes the steps of rough target positioning by successively opening
DMD blocks and accurate target positioning by adjusting the rough areas via intelligent search algorithm. Simulation
experiments were carried out to compare the proposed method with traditional method. The result shows the area of the
ships are accurately positioned without reconstructing the image by the proposed method and the multi-level scale
imaging for suspect areas is realized. Compared with traditional target detection method from the reconstructed image,
the proposed method not only highly enhances the measuring and reconstruction efficiency but also improves the
positioning accuracy, which would be more significant for large area scene.