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We propose a diffuser-based lensless underwater optical signal detection system. The system consists of a lensless one-dimensional (1D) camera array equipped with random phase modulators for signal acquisition and one-dimensional integral imaging convolutional neural network (1DInImCNN) for signal classification. During the acquisition process, the encoded signal transmitted by a light-emitting diode passes through a turbid medium as well as partial occlusion. The 1D diffuser-based lensless camera array is used to capture the transmitted information. The captured pseudorandom patterns are then classified through the 1DInImCNN to output the desired signal. We compared our proposed underwater lensless optical signal detection system with an equivalent lens-based underwater optical signal detection system in terms of detection performance and computational cost. The results show that the former outperforms the latter. Moreover, we use dimensionality reduction on the lensless pattern and study their theoretical computational costs and detection performance. The results show that the detection performance of lensless systems does not suffer appreciably. This makes lensless systems a great candidate for low-cost compressive underwater optical imaging and signal detection.
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COMPARISON OF DIVER-OPERATED UNDERWATER PHOTOGRAMMETRIC SYSTEMS FOR CORAL REEF MONITORING
Abstract. Underwater photogrammetry is a well-established technique for measuring and modelling the subaquatic environment in fields ranging from archaeology to marine ecology. While for simple tasks the acquisition and processing of images have become straightforward, applications requiring relative accuracy better then 1:1000 are still considered challenging. This study focuses on the metric evaluation of different off-the-shelf camera systems for making high resolution and high accuracy measurements of coral reefs monitoring through time, where the variations to be measured are in the range of a few centimeters per year. High quality and low-cost systems (reflex and mirrorless vs action cameras, i.e. GoPro) with multiple lenses (prime and zoom), different fields of views (from fisheye to moderate wide angle), pressure housing materials and lens ports (dome and flat) are compared. Tests are repeated at different camera to object distances to investigate distance dependent induced errors and assess the accuracy of the photogrammetrically derived models. An extensive statistical analysis of the different systems is performed and comparisons against reference control point measured through a high precision underwater geodetic network are reported.
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- Award ID(s):
- 1637396
- PAR ID:
- 10093045
- Date Published:
- Journal Name:
- ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
- Volume:
- XLII-2/W10
- ISSN:
- 2194-9034
- Page Range / eLocation ID:
- 143 to 150
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/isprs-archives-XLII-2-W10-143-2019
