  LiDAR (Light Detection and Ranging) technology has remained popular in
capturing natural and built environments for numerous applications. The recent
technological advancements in electro-optical engineering have aided in
obtaining laser returns at a higher pulse repetition frequency (PRF), which
considerably increased the density of the 3D point cloud. Conventional
techniques with lower PRF had a single pulse-in-air (SPIA) zone, large enough
to avoid a mismatch among pulse pairs at the receiver. New multiple
pulses-in-air (MPIA) technology guarantees various windows of operational
ranges for a single flight line and no blind zones. The disadvantage of the
technology is the projection of atmospheric returns closer to the same
pulse-in-air zone of adjacent terrain points likely to intersect with objects
of interest. These noise properties compromise the perceived quality of the
scene and encourage the development of new noise-filtering neural networks, as
existing filters are significantly ineffective. We propose a novel
dual-attention noise-filtering neural network called Noise Seeking Attention
Network (NSANet) that uses physical priors and local spatial attention to
filter noise. Our research is motivated by two psychology theories of feature
integration and attention engagement to prove the role of attention in computer
vision at the encoding and decoding phase. The presented results of NSANet show
the inclination towards attention engagement theory and a performance boost
compared to the state-of-the-art noise-filtering deep convolutional neural
networks.
