scholarly journals Background Information Self-Learning Based Hyperspectral Target Detection

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yufei Tian ◽  
Jihai Yang ◽  
Shijun Li ◽  
Wenning Xu
Keyword(s):  
Information Extraction ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Background Information ◽  
Spectral Information ◽  
Tumor Region ◽  
Self Learning

Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.

scholarly journals Compact spatio-spectral algorithm for single image super-resolution in hyperspectral imaging

2016 ◽  
Vol 36 (3) ◽  
pp. 117
Author(s):  
Miguel Angel Marquez Castellanos ◽  
Cesar Augusto Vargas ◽  
Henry Arguello
Keyword(s):  
Hyperspectral Imaging ◽  
Three Dimensional ◽  
Super Resolution ◽  
Accurate Estimate ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Single Image ◽  
Wide Range ◽  
Spectral Algorithm ◽  
Single Image Super Resolution

Hyperspectral imaging (HSI) is used in a wide range of applications such as remote sensing, space imagery, mineral detection, and exploration. Unfortunately, it is difficult to acquire hyperspectral images with high spatial and spectral resolution due to instrument limitations. The super-resolution techniques are used to reconstruct low-resolution hyperspectral images. However, traditional superresolution (SR) approaches do not allow direct use of both spatial and spectral information, which is a decisive for an optimal reconstruction. This paper proposes a single image SR algorithm for HSI. The algorithm uses the fact that the spatial and spectral information can be integrated to make an accurate estimate of the high-resolution HSI. To achieve this, two types of spatio- pectral downsampling, and a three-dimensional interpolation are proposed in order to increase coherence between the spatial and spectral information. The resulting reconstructions using the proposed method are up to 2 dB better than traditional SR approaches.

An Optical Flow-Based PIG Method for Active Contour Tracking Initialization

2013 ◽  
Vol 760-762 ◽  
pp. 1311-1316
Author(s):  
Jun Ru Wang ◽  
Xiao Feng Lu ◽  
Yu Chen Wang ◽  
Tian Xiang Fan
Keyword(s):  
Optical Flow ◽  
Motion Detection ◽  
Active Contour ◽  
Detection Method ◽  
Experimental Results ◽  
Background Information ◽  
Moving Target ◽  
Contour Tracking ◽  
Target Region ◽  
Correct Target

In this paper, we propose a new optical flow-based Poisson inverse gradient (OFPIG) initialization method for active contour tracking. This method can automatically initialize the contour of moving target for consequent tracking. First, an optical flow based motion detection method is adopted to remove background information, and then a Poisson inverse gradient (PIG) initialization is applied to locate the target region. Finally, parametric active contour is used to evolve the correct target contour depending on the initialization precision. Experimental results have demonstrated its effectiveness and robustness.

scholarly journals A COMPARATIVE ANALYSIS OF SPECTRAL INFORMATION EXTRACTION ALGORITHMS FOR TARGET DETECTION OF HYDROTHERMAL ALTERATION ZONES USING ASTER SATELLITE IMAGE DATA

2020 ◽  
Vol V-3-2020 ◽  
pp. 649-654
Author(s):  
A. J. Abubakar ◽  
M. Hashim ◽  
A. B. Pour ◽  
Y. Saleh
Keyword(s):  
Information Extraction ◽  
Target Detection ◽  
Hydrothermal Alteration ◽  
Satellite Image ◽  
Image Data ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Alteration Zones ◽  
Satellite Image Data ◽  
Hydrothermal Alteration Zones

Abstract. The focus of this paper is to evaluate the performance of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs as proxy for narrowing areas of interest. The study employed the Per-pixel Spectral Angle Mapper (SAM) and the Sub-pixel Linear Spectral Unmixing (LSU) algorithms for spectral information extraction by using the ASTER satellite image data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results of the analysis show that both SAM and LSU discriminated targets of interest better when employing image spectra and poorly when using library spectra. However, the Per-pixel SAM is unsuitable for target detection and more suited where the objective of the investigation is to classify whole scene and not particular targets as in this case. The LSU was found to be effective for discriminating alterations associated with the thermal springs especially where image endmember spectra are employed for analysis, thus recommended for prefeasibility mapping of GT related resources.

scholarly journals Hardware Detection Method of Transmission Line Patrol Inspection Image Based on Improved YOLOV4 Model

2020 ◽  
Author(s):  
Ning Wang ◽  
Hanghang Zhao ◽  
Wulue Zheng ◽  
Chaoshuo Wang
Keyword(s):  
Fault Diagnosis ◽  
Target Detection ◽  
Performance Index ◽  
Detection Method ◽  
Experimental Results ◽  
Mean Average Precision ◽  
Aerial Images ◽  
State Detection ◽  
K 12 ◽  
Detection Effect

In order to solve the problem of intelligent hardware detection in aerial images, a hardware target detection method based on improved YOLOV4 model is proposed. In order to solve the problems of dense hardware and occlusion in aerial images, the improved network based on channel and spatial hybrid attention mechanism can further improve the detection effect of dense occlusion hardware and reduce image false detection and missed detection. In order to solve the problem that there is a great error in the position of the detection frame caused by the interference between the hardware and the hardware and between the hardware and the background, the prior frame is optimized by K-means++, and it is determined that the anchors generated by K=12 is the best, and the detection boxes are more suitable for the target. The experimental results show that the proposed method solves the problems of missing detection, misdetection and inaccurate detection frame to some extent, in which the mAP (mean Average Precision) value of the performance index is increased from 65.03% to 70.72%. The research can lay a good foundation for further state detection and fault diagnosis of typical hardware.

scholarly journals Vehicle Detection in Very-High-Resolution Remote Sensing Images Based on an Anchor-Free Detection Model with a More Precise Foveal Area

2021 ◽  
Vol 10 (8) ◽  
pp. 549
Author(s):  
Xungen Li ◽  
Feifei Men ◽  
Shuaishuai Lv ◽  
Xiao Jiang ◽  
Mian Pan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Detection Method ◽  
Vehicle Detection ◽  
Aerial Images ◽  
Background Information ◽  
Remote Sensing Images ◽  
Feature Pyramid ◽  
Very High

Vehicle detection in aerial images is a challenging task. The complexity of the background information and the redundancy of the detection area are the main obstacles that limit the successful operation of vehicle detection based on anchors in very-high-resolution (VHR) remote sensing images. In this paper, an anchor-free target detection method is proposed to solve the problems above. First, a multi-attention feature pyramid network (MA-FPN) was designed to address the influence of noise and background information on vehicle target detection by fusing attention information in the feature pyramid network (FPN) structure. Second, a more precise foveal area (MPFA) is proposed to provide better ground truth for the anchor-free method by determining a more accurate positive sample selection area. The proposed anchor-free model with MA-FPN and MPFA can predict vehicles accurately and quickly in VHR remote sensing images through direct regression and predict the pixels in the feature map. A detailed evaluation based on remote sensing image (RSI) and vehicle detection in aerial imagery (VEDAI) data sets for vehicle detection shows that our detection method performs well, the network is simple, and the detection is fast.

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