Moving Target Detection with Modified Logarithm Background Subtraction and Its Application to the GF-3 Spotlight Mode

Abstract

Spaceborne spotlight SAR mode has drawn attention due to its high-resolution capability, however, the studies about moving target detection with this mode are less. The paper proposes an image sequence-based method entitled modified logarithm background subtraction to detect ground moving targets with Gaofen-3 Single Look Complex (SLC) spotlight SAR images. The original logarithm background subtraction method is designed by our team for airborne SAR. It uses the subaperture image sequence to generate a background image, then detects moving targets by using image sequence to subtract background. When we apply the original algorithm to the spaceborne spotlight SAR data, a high false alarm problem occurs. To tackle the high false alarm problem due to the target’s low signal-to-noise-ratio (SNR) in spaceborne cases, several improvements are made. First, to preserve most of the moving target signatures, a low threshold CFAR (constant false alarm rate) detector is used to get the coarse detection. Second, because the moving target signatures have higher density than false detections in the coarse detection, a modified DBSCAN (density-based spatial-clustering-of-applications-with-noise) clustering method is then adopted to reduce false alarms. Third, the Kalman tracker is used to exclude the residual false detections, due to the real moving target signature having dynamic behavior. The proposed method is validated by real data, the shown results also prove the feasibility of the proposed method for both Gaofen-3 and other spaceborne systems.

Static High Target-Induced False Alarm Suppression in Circular Synthetic Aperture Radar Moving Target Detection Based on Trajectory Features

“…Specifically, Figure 4a shows the original images and Figure 3 illustrates the rotation phenomenon of billboards; Figure 3a displays its optical image while Figure 3b presents its rotations in 4 sub-images. For instance, the detection results obtained using the logarithm background subtraction (LBS) method [24,25] are shown in Figure 4, where the green box indicates the moving target, and the red circle represents the billboard. Specifically, Figure 4a shows the original images and Figure 4b shows the corresponding detection result, which identifies that the billboard remains after applying the LBS method.…”

“…The logarithm background subtraction (LBS) method represents a single-channel CSAR moving target detection algorithm [24,25]. This algorithm extracts background (i.e., static scene) images from the sequence, followed by subsequent background subtraction procedures to facilitate detection of the moving target-containing foreground image.…”

“…On the other hand, the latter method detects the target's smear/displaced signature in image sequences, which has fewer constraints and is therefore more applicable. One of the typical image sequence-based methods is logarithm background subtraction (LBS) [24,25], which uses a median filter to obtain the static background image, and then extracts the moving target signature by subtracting it from the original images. Another typical example is low-rank-based methods like the Robust Principal Component Analysis (RPCA) and Go Decomposition (GoDec) [26][27][28].…”

A comparison of algorithms to suppress noise in GF-3 FSII SAR images

“…The GF-3(GaoFen-3) synthetic aperture radar (SAR) imaging satellite was launched on August 10, 2016. GF-3, which is characterized by full-polarization observation, has been widely used in monitoring and information extraction of regions such as oceans, forests, and urban landscapes, and has been applied in many fields, such as disaster assessment [1][2][3][4][5][6][7] . The process of SAR imaging inevitably generates multiplicative speckle noise [8] , which complicates the post-processing and analysis of SAR images [9][10][11][12][13] .…”

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types

“…This could be particularly important as the temporal components of a trajectory could be missing or unreliable. For example, in the case of a trajectory that was inferred from an aerial image containing the traces of a moving object [ 22 , 23 ], the temporal component would be missing altogether or in the cases of a trajectory that was based on a sequence of images with a low temporal resolution (for example, refer to [ 24 , 25 ]), the kinematic descriptors may not be reliable. Additionally, it is common for the time reporting mechanism of a GPS device to glitch and cause “uncertainty” [ 2 ].…”