scholarly journals Anomaly targets detection of hyperspectral imagery based on wavelet transform and sparse representation

2018 ◽  
Vol 232 ◽  
pp. 02054
Author(s):  
Cheng Baozhi
Keyword(s):  
Wavelet Transform ◽  
Sparse Representation ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Image Data ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Low Efficiency

The research of anomaly target detection algorithm in hyperspectral imagery is a hot issue, which has important research value. In order to overcome low efficiency of current anomaly target detection in hyperspectral image, an anomaly detection algorithm for hyperspectral images based on wavelet transform and sparse representation was proposed. Firstly, two-dimensional discrete wavelet transform is used to denoise the hyperspectral image, and the new hyperspectral image data are obtained. Then, the results of anomaly target detection are obtained by using sparse representation theory. The real AVIRIS hyperspectral imagery data sets are used in the experiments. The results show that the detection accuracy and false alarm rate of the propoesd algorithm are better than RX and KRX algorithm.

Locality Preserving Projection Based on Endmember Extraction for Hyperspectral Image Dimensionality Reduction and Target Detection

2016 ◽  
Vol 70 (9) ◽  
pp. 1573-1581 ◽  
Author(s):  
Yiting Wang ◽  
Shiqi Huang ◽  
Zhigang Liu ◽  
Hongxia Wang ◽  
Daizhi Liu
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Detection Accuracy ◽  
Endmember Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving ◽  
Weighted Matrix

In order to reduce the effect of spectral variability on calculation precision for the weighted matrix in the locality preserving projection (LPP) algorithm, an improved dimensionality reduction method named endmember extraction-based locality preserving projection (EE-LPP) is proposed in this paper. The method primarily uses the vertex component analysis (VCA) method to extract endmember spectra from hyperspectral imagery. It then calculates the similarity between pixel spectra and the endmember spectra by using the spectral angle distance, and uses it as the basis for selecting neighboring pixels in the image and constructs a weighted matrix between pixels. Finally, based on the weighted matrix, the idea of the LPP algorithm is applied to reduce the dimensions of hyperspectral image data. Experimental results of real hyperspectral data demonstrate that the low-dimensional features acquired by the proposed methods can fully reflect the characteristics of the original image and further improve target detection accuracy.

scholarly journals Object Detection from the Video Taken by Drone via Convolutional Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Chenfan Sun ◽  
Wei Zhan ◽  
Jinhiu She ◽  
Yangyang Zhang
Keyword(s):  
Object Detection ◽  
Target Detection ◽  
Image Data ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Detection Systems ◽  
Video Frames ◽  
Performance Effects ◽  
And Performance

The aim of this research is to show the implementation of object detection on drone videos using TensorFlow object detection API. The function of the research is the recognition effect and performance of the popular target detection algorithm and feature extractor for recognizing people, trees, cars, and buildings from real-world video frames taken by drones. The study found that using different target detection algorithms on the “normal” image (an ordinary camera) has different performance effects on the number of instances, detection accuracy, and performance consumption of the target and the application of the algorithm to the image data acquired by the drone is different. Object detection is a key part of the realization of any robot’s complete autonomy, while unmanned aerial vehicles (UAVs) are a very active area of this field. In order to explore the performance of the most advanced target detection algorithm in the image data captured by UAV, we have done a lot of experiments to solve our functional problems and compared two different types of representative of the most advanced convolution target detection systems, such as SSD and Faster R-CNN, with MobileNet, GoogleNet/Inception, and ResNet50 base feature extractors.

Comparative Studies of Target Detection Algorithm for Hyperspectral Imagery

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
B K Nagesha ◽  
M R Puttaswamy ◽  
Dsouza Hasmitha ◽  
G Hemantha Kumar
Keyword(s):  
Comparative Study ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
Hyperspectral Data ◽  
Detection Algorithms ◽  
Complex Process

<p>Target detection in hyperspectral imagery is a complex process due to many factors. Exploiting the hyperspectral image<br />for analysis is very challenging due to large information and low spatial resolution. However, hyperspectral target<br />detection has numerous applications. Hence, it is important to pursue research in target detection. In this paper, a<br />comparative study of target detection algorithms for hyperspectral imagery is presented along with scope for future<br />research. A comparative study behind the hyperspectral imaging is detailed. Also, various challenges involved in<br />exploring the hyperspectral data are discussed.</p>

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

Study for Detection Algorithm of QRS Complex in ECG Signal

2013 ◽  
Vol 765-767 ◽  
pp. 2105-2108
Author(s):  
Xu Wen Li ◽  
Bi Wei Zhang ◽  
Qiang Wu
Keyword(s):  
Wavelet Transform ◽  
Detection Algorithm ◽  
Automatic Analysis ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Good Precision ◽  
Qrs Complex ◽  
Noise Interference ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

In ECG signals accurate detection to the position of QRS complex is a key to automatic analysis and diagnosis system. And its premise is that effectively remove all kinds of noise interference in ECG signal. Here, a method of detecting QRS based on EMD and wavelet transform was presented which is aim to improve the anti-noise performance of the detection algorithm. It is combined EMD with the theory of singularity detecting based on wavelet transform modulus maxima method. It has the high detection accuracy and good precision that can give an effective way to the automatic analysis for ECG signal.

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