Single-pixel imaging (SPI) is a novel technique that captures 2D images using a programmable spatial light modulator (SLM) and a single-pixel detector, instead of conventional 2D array sensors. The image can be reconstructed from the modulation patterns and corresponding 1D bucket measurements. Conventional object detection is performed after a reconstructed image with high fidelity is available. In this paper, an image-free object detection method using the single-pixel measurements is proposed. We designed and trained an end-to-end convolutional neural network to encode and decode a scene for image-free object detection application. The performance of the proposed method is demonstrated using part data from the Voc dataset, which achieves a detection accuracy of 35.41% mAP at a sampling rate below 1.6%.
Intertidal terrain extraction based on satellite remote sensing images is of great military application and civilian value. In this paper, we propose a fusion algorithm based on SAR and optical remote sensing image to enhance intertidal feature. With the SAR and panchromatic images from different phases, we use an improved shift-invariant discrete wavelet transform algorithm to obtain higher quality images that can not only highlight the shape of the intertidal zone, but also highlight the characteristics of the coastline. The real data experiment and comparison experiments with other algorithms show that the algorithm proposed in this paper is effective in enhancing the features of the intertidal zone.
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