Research on Remote Sensing Image Matching with Special Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

Implementation of the adaptive quantization method in digitally controlled measuring generators

Measuring generators with digital control, in particular power calibrators, used to calibrate electricity meters, contain a digital-to-analog converter (DAC) that converts codes of the generated signal into voltage. Signal codes are stored in the generator memory. A truncation discreteness error (quantization noise) arises caused by sampling (quantization) in time and by the level of signal samples in the DAC. A relative value of the quantization noise depends on the amplitude of the generated signal (relative to the reference voltage of the DAC): the larger the amplitude, the more significant bits of the DAC are involved in the conversion process, and the less the relative value of the noise. In generators, where the amplitude of the output signal changes over a wide range (high dynamic range) by changing the digital samples of the signal, the quantization noise at low signal amplitudes can become unacceptably large. This situation occurs in power calibrators where the output current changes hundreds of times since the error of the verified electricity meter is normalized in a wide range of current flowing through it. A new algorithm for generating samples of a sinusoidal signal in measuring generators with digital control called adaptive quantization is proposed. Adaptive quantization can significantly improve one of the selected signal parameters (the so-called optimality criterion), for example, reduce the error in reproduction of the first harmonic, or reduce the value of higher harmonic components. In addition, the proposed algorithm reduces the dependence of the selected parameter on the sampling frequency and on the number of DAC bits used, which makes it possible to expand the dynamic range of the generator (in the current channel) without using additional amplifiers with programmable gain (PGA). Studies carried out using computer simulation have confirmed the efficiency of the adaptive quantization algorithm.