Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

Chen, Guobin; Sun, Zhigang; Zhang, Li

doi:10.1109/access.2020.3012997

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…Using just one labeled picture, a new framework for weakly supervised transfer learning [22] is described in this process to categorize multitemporal remote sensing images. [23] Our system can categorize all the other multitemporal images chronologically without any labeling effort by exploiting the consistency of timeseries images and a domain adaption mechanism. [24] Our system obtains a classification accuracy that is comparable to what would be obtained with [25] supervised learning.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

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Section: Introductionmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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“…According to the experimental data in Figure 4, we can conclude that the relative difference of the main melody within the interval of X(1)-X (16) fluctuates greatly. When the interval variable is X(3), the relative difference is the largest.…”

Section: Simulation Experimentsmentioning

confidence: 83%

“…e music collection module is composed of two parts, namely, the collection submodule and the encoding module. e music collection submodule is composed of sound sensors installed in different positions and is responsible for collecting the original music signal [16]. e sound sensor has a built-in capacitive electret microphone that is sensitive to sound, which is converted by an A/D converter and transmitted to the voice coding submodule [17].…”

Section: Design Of Music Collection Modulementioning

confidence: 99%

Aided Recognition and Training of Music Features Based on the Internet of Things and Artificial Intelligence

Zhang

2022

Computational Intelligence and Neuroscience

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With the development of the Internet of Things, many industries have been on the train of the information age, and digital audio technology is also constantly developing. Music retrieval has gradually become a research hotspot in the music industry. Among them, the auxiliary recognition of music characteristics is also a particularly important Task. Music retrieval is mainly to manually extract music signals, but now the music signal extraction technology has encountered a bottleneck. The article uses Internet and artificial intelligence technology to design an SNN music feature recognition model to identify and classify music features. The research results of the article show (1) statistic graphs of the main melody and accompanying melody of different music. The absolute value of the main melody and accompanying melody mainly fluctuates in the range of 0–7, and the proportion of the main melody can reach 36%. The accompanying melody can reach 17%. After the absolute value of the interval reaches 13, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.6 and 0.9, and the melody interval ratio value completely coincides; the main melody in the interval variable is X. (1) The relative difference value in the interval of −X(16) fluctuates greatly. After the absolute value of the interval reaches 17, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.01 and 0.04 and the main melody. The value of the difference is always higher than the accompanying melody. (2) When the number of feature maps is 24 ∗ 5 , the recognition result is the most accurate, MAP recognition result can reach 78.8, and the recognition result of precision@ is 79.2; when the feature map size is 5 ∗ 5 , the recognition result is the most accurate, MAP recognition result can reach 78.9, the recognition result of precision@ is 79.2, and the recognition result of HAM2 (%) is 78.6. The detection accuracy of the SNN music recognition model proposed in the article is the highest. When the number of bits is 64, the detection accuracy of the SNN detection model is 59.2%, and the detection accuracy of the improved SNN music recognition model is 79.3%, which is better than the detection rate of ITQ music recognition model of 17.9%, which is 61.4% higher. The experimental data further shows that the detection efficiency of the ITQ music recognition model is the highest. (3) The SNN music recognition model proposed in the article has the highest detection accuracy, regardless of whether it is in a noisy or no-noise music environment, with an accuracy rate of 97.97% and a detection accuracy value of 0.88, which is 5 types of music. The highest one among the recognition models, the ITQ music recognition model, has the lowest detection accuracy, with a detection accuracy of 67.47% in the absence of noise and a detection accuracy of 70.23% in the presence of noise. Although there is a certain noise removal technology, it can suppress noise interference to a certain extent, but cannot accurately describe music information, and the detection accuracy rate is also low.

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“…It can effectively protect the edges of the image after denoising and try to avoid blurring [14]. Wavelet filtering is widely used in timefrequency analysis and multi-scale analysis [15]. It can effectively filter random noise mixed in high-frequency signals and distinguish defects and interference points in the image [16].…”

Section: Reelated Workmentioning

confidence: 99%

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

Yang

Zhang

et al. 2022

IEEE Access

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The surface defect detection of automobile pipe joints based on computer vision faces technical challenges. The tiny-sized and smooth surfaces with processing textures will undermine the defect detection accuracy. In order to solve this problem, a new method was proposed, which combines wavelet decomposition and reconstruction with the canny operator to detect defects, and then uses the multi-channel fusion convolutional neural network to identify the types of defects. Firstly, illumination compensation technology is used to obtain a more uniform gray distribution of the original image. Then, the wavelet decomposition and reconstruction are used to remove noises and processing textures. Furthermore, the defect regions are segmented using the canny operator and hole filling from the image. Finally, the multichannel fusion convolutional neural network of decision-level is used to identify the surface defect types. This method provides an idea for the surface defects detection of automobile pipe joints with serious interference, such as smooth surface, random noises, and processing textures. The experimental results reveal that the method can effectively eliminate the influence of uneven illumination, random noises, and processing textures and achieve high defect classification accuracy.INDEX TERMS Automobile pipe joint, surface defect detection, wavelet decomposition and reconstruction, multi-channel fusion convolutional neural network.

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Road Identification Algorithm for Remote Sensing Images Based on Wavelet Transform and Recursive Operator

Cited by 18 publications

References 24 publications

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Aided Recognition and Training of Music Features Based on the Internet of Things and Artificial Intelligence

Image Classification for Automobile Pipe Joints Surface Defect Detection Using Wavelet Decomposition and Convolutional Neural Network

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