Automatic boat detection based on diffusion and radiation characterization of boat lights during night for VIIRS DNB imaging data

Abstract

Visible infrared imaging radiometer suite (VIIRS) day/night band (DNB) data has been used to detect lit boats during night as it is very sensitive to low radiances. The existing methods for boat detection from VIIRS DNB data are mainly based on thresholds that are estimated by the statistical characteristics of pixels or artificial experience. This may generate detection errors and poor adaptability due to the lack of characterization of boat lights. In this paper, a two-step threshold detection algorithm based on the point spread and the radiative characteristics of nightlight point sources is proposed, so that the interference from adjacent pixels could be reduced as much as possible and a reasonable threshold could be determined. Meanwhile, this algorithm is applied to three study areas, namely the sea area around Tianjin Port in Bohai Sea, the sea area around Shanghai Port in East China Sea, and the sea area around Port Sulphur in Gulf of Mexico. It is demonstrated that the detection precision of the proposed algorithm reaches up to 90% and the recall rate reaches up to 85% in three areas when validated by visual interpretation, and the precision is 85.71% when validated by automatic identification system (AIS) data in the study area of the sea area around Port Sulphur in Gulf of Mexico, which approximately increases by 5% compared with the previous algorithm.

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

“…Currently, NTL satellite-based vessel detection methods can be broadly classified into two types, namely, threshold-based methods [13][14][15] and machine learning approaches [16,17] The predominant focus of threshold-based research for identifying illuminated vessels centers on leveraging the significant radiometric contrast between illuminated vessels and the oceanic backdrop. For instance, Elvidge et al [13] conducted a detailed analysis of spike features within VIIRS/DNB data, resulting in the identification of vessel detections that include strong, weak, and diffused signals, eliminating non-targeted noise from cloud-scattered light.…”

“…Considering that the algorithms produce many false detections under full-moon conditions, Kim et al [14] considered the moon phase as a crucial factor influencing the detection threshold, enabling detection through the application of a single specific threshold instead of different thresholds after the relative correction. Xue et al [15] addressed interference from adjacent pixels and established a rational threshold by implementing a two-step threshold-detection algorithm based on radiometry equations and the diffusion characteristics of nightlight points. Nevertheless, the conventional reliance on manually crafted features within threshold-based methods poses challenges in ensuring robust detection accuracy, given the intricate nature of real-world scenarios and the sheer volume of data.…”

Vessel Detection with SDGSAT-1 Nighttime Light Images

“…Kim Euihyun et al [21] introduced the lunar phase into the preprocessing stage and calculated the bias parameter used for correction through an empirical model for lunar phase correction. Xue et al [22] processed noise information through adaptive filtering and quantitatively calculated and compensated for the loss of light sources spreading by establishing atmospheric transmission and diffusion models.…”