Compressive Bidirectional Reflection Distribution Function-Based Feature Extraction Method for Camouflaged Object Segmentation

Camouflaged target segmentation has been widely used in both civil and military applications, such as wildlife behaviour monitoring, crop pest control, and battle reconnaissance. However, it is difficult to distinguish camouflaged objects and natural backgrounds using traditional grey-level feature extraction. In this paper, a compressive bidirectional reflection distribution function-based feature extraction method is proposed for effective camouflaged object segmentation. First, multidimensional grey-level features are extracted from multiple images with different illumination angles in the same scene. Then, the multidimensional grey-level features are expanded based on Chebyshev polynomials. Next, the first several coefficients are integrated as a new optical feature, which is named the compressive bidirectional reflection distribution function feature. Finally, the camouflaged object can be effectively segmented from the background by compressive feature clustering. Both qualitative and quantitative experimental results prove that our method has remarkable advantages over conventional single-angle or multi-angle grey-level feature-based methods in terms of segmentation precision and running speed

Compressive Bidirectional Reflection Distribution Function-Based Feature Extraction Method for Camouflaged Object Segmentation

Camouflaged target segmentation has been widely used in both civil and military applications, such as wildlife behaviour monitoring, crop pest control, and battle reconnaissance. However, it is difficult to distinguish camouflaged objects and natural backgrounds using traditional grey-level feature extraction. In this paper, a compressive bidirectional reflection distribution function-based feature extraction method is proposed for effective camouflaged object segmentation. First, multidimensional grey-level features are extracted from multiple images with different illumination angles in the same scene. Then, the multidimensional grey-level features are expanded based on Chebyshev polynomials. Next, the first several coefficients are integrated as a new optical feature, which is named the compressive bidirectional reflection distribution function feature. Finally, the camouflaged object can be effectively segmented from the background by compressive feature clustering. Both qualitative and quantitative experimental results prove that our method has remarkable advantages over conventional single-angle or multi-angle grey-level feature-based methods in terms of segmentation precision and running speed

Infrared Ocean Image Simulation Algorithm Based on Pierson–Moskowitz Spectrum and Bidirectional Reflectance Distribution Function

Infrared ocean image simulation has been widely used in water-pollution prevention, meteorological observation and melting-ice monitoring. However, in actual remote sensing observation scenes, the simulation images provided by conventional algorithms are lacking sufficient wave details because the viewing angle and the scale of simulation images are simplex. In this paper, an infrared ocean image simulation algorithm based on the Pierson–Moskowitz spectrum and a bidirectional reflectance distribution function is proposed. First, a 3D model of ocean surface is set up based on Pierson–Moskowitz spectrum. Then, the imaging position is calculated by the pinhole camera imaging method, which describes how each point of the 3D model is mapping to the 2D image. Next, by using a bidirectional reflectance distribution function, the radiation intensity from every point of the ocean model to the camera is computed. Finally, we figure up the sum of the radiation intensity received by every point of the detector and obtain the infrared simulation ocean image by quantizing the radiation intensity sum to grayscale. The entropy of the simulation images is 2.725, which is, respectively, improved by 71.86% and 16.83% compared with two other algorithms. The Kullback–Leibler divergence of the simulation images is 11.446, which is improved by 0.54% and 0.59% compared with other algorithms. The quantitative experimental results prove that the authenticity and clarity of the presented simulation images have remarkable advantages over conventional algorithms

Infrared Ocean Image Simulation Algorithm Based on Pierson–Moskowitz Spectrum and Bidirectional Reflectance Distribution Function

Infrared ocean image simulation has been widely used in water-pollution prevention, meteorological observation and melting-ice monitoring. However, in actual remote sensing observation scenes, the simulation images provided by conventional algorithms are lacking sufficient wave details because the viewing angle and the scale of simulation images are simplex. In this paper, an infrared ocean image simulation algorithm based on the Pierson–Moskowitz spectrum and a bidirectional reflectance distribution function is proposed. First, a 3D model of ocean surface is set up based on Pierson–Moskowitz spectrum. Then, the imaging position is calculated by the pinhole camera imaging method, which describes how each point of the 3D model is mapping to the 2D image. Next, by using a bidirectional reflectance distribution function, the radiation intensity from every point of the ocean model to the camera is computed. Finally, we figure up the sum of the radiation intensity received by every point of the detector and obtain the infrared simulation ocean image by quantizing the radiation intensity sum to grayscale. The entropy of the simulation images is 2.725, which is, respectively, improved by 71.86% and 16.83% compared with two other algorithms. The Kullback–Leibler divergence of the simulation images is 11.446, which is improved by 0.54% and 0.59% compared with other algorithms. The quantitative experimental results prove that the authenticity and clarity of the presented simulation images have remarkable advantages over conventional algorithms