Multi-Modal Remote Sensing Image Matching Method Based on Deep Learning Technology

Remote sensing is a scientific technology that uses sensors to detect the reflection, radiation or scattering of electromagnetic wave signals from ground objects in a non-contact and long-distance manner. The images are classified by the extracted image feature information Recognition is a further study of obtaining target feature information, which is of great significance to urban planning, disaster monitoring, and ecological environment evaluation. The image matching framework proposed in this paper matches the depth feature maps, and reversely pushes the geometric deformation between the depth feature maps to between the original reference image and the target image, and eliminates the geometric deformation between the original images. Finally, through feature extraction of the corrected image, the extracted local feature image blocks are input into the trained multi-modal feature matching network to complete the entire matching process. Experiments show that the negative sample set construction strategy that takes into account the sample distance proposed in this experiment can effectively deal with the problem of neighboring point interference in RSI matching, and improve the matching performance of the network model.

Localization Of A Mobile Fastening Robot

<div>The goal of this research is to develop a localization system for a mobile fastening robot using a camera and ultrasonic sensors. Localization is performed by using triangulation methods on three target fastener heads. Camera calibration parameters are determined and used to obtain a corrected image on which a Circular Hough Transform algorithm is used to determine the location of the three target fastener heads relative to the camera. The distance to the fastener heads is determined using readings from two ultrasonic sensors. A Kalman Filter is developed and used to reduce the noise of the ultrasonic sensor readings. In addition to filtering, calibration techniques are used to correct the readings of the final localization system. Testing of the complete system is done using a coordinate measuring machine. </div>

Localization Of A Mobile Fastening Robot

<div>The goal of this research is to develop a localization system for a mobile fastening robot using a camera and ultrasonic sensors. Localization is performed by using triangulation methods on three target fastener heads. Camera calibration parameters are determined and used to obtain a corrected image on which a Circular Hough Transform algorithm is used to determine the location of the three target fastener heads relative to the camera. The distance to the fastener heads is determined using readings from two ultrasonic sensors. A Kalman Filter is developed and used to reduce the noise of the ultrasonic sensor readings. In addition to filtering, calibration techniques are used to correct the readings of the final localization system. Testing of the complete system is done using a coordinate measuring machine. </div>

LOCALIZATION OF A MOBILE FASTENING ROBOT

<div>The goal of this research is to develop a localization system for a mobile fastening robot using a camera and ultrasonic sensors. Localization is performed by using triangulation methods on three target fastener heads. Camera calibration parameters are determined and used to obtain a corrected image on which a Circular Hough Transform algorithm is used to determine the location of the three target fastener heads relative to the camera. The distance to the fastener heads is determined using readings from two ultrasonic sensors. A Kalman Filter is developed and used to reduce the noise of the ultrasonic sensor readings. In addition to filtering, calibration techniques are used to correct the readings of the final localization system. Testing of the complete system is done using a coordinate measuring machine. </div>

Quantitative Analysis of Multiple Food Additives in Wheat Flour by Raman Hyperspectral Imaging

Raman hyperspectral imaging can obtain both the internal Raman signals and the external image information of the sample simultaneously. This study investigated the quantitatively analysis of multiple food additives in wheat flour by using this technology. Raman hyperspectral images of wheat flour containing the three additives, L-ascorbate acid (LAA), azodicarbonamide (ADC) and benzoyl peroxide (BPO), were collected. Raman signals in Raman hyperspectral images were preprocessed by smoothing and baseline correction methods to obtain the corrected image. Chemical images were created to visually identify additive pixels by selecting single-band image corresponding to Raman characteristic peaks of each additive from the corrected image and combining with the threshold segmentation method. The results showed that the chemical image can identify the above three additives in wheat flour. The identified additive pixels have a significant linear relationship with their concentration, and the coefficients of determination of LAA, ADC and BPO in the quantitative model were 0.9858, 0.9868 and 0.9830, respectively. This study indicated that the Raman characteristic peaks and threshold segmentation provide a non-destructive method for quantitative analysis of multiple wheat flour additives in Raman hyperspectral images.

Fast Energy Dependent Scatter Correction for List-Mode PET Data

Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view.

Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

Sentinel-2 and Landsat 8 satellites constitute an unprecedented source of freely accessible satellite imagery. To produce precise outputs from the satellite data, however, proper use of atmospheric correction methods is crucial. In this work, we tested the performance of six different atmospheric correction methods (QUAC, FLAASH, DOS, ACOLITE, 6S, and Sen2Cor), together with atmospheric correction given by providers, non-corrected image, and images acquired using an unmanned aerial vehicle while working with the normalised difference vegetation index (NDVI) as the most widely used index. We tested their performance across urban, rural, and vegetated land cover types. Our results show a substantial impact from the choice of the atmospheric correction method on the resulting NDVI. Moreover, we demonstrate that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery.

Enhanced Region of Interest Extraction method for Finger Vein Images

Extracting Region of Interest (ROI) is an important step for finger vein recognition system. The purpose of this process is to determine the part of the image that we need for extracting features. In this paper we present an ROI extraction method that overcome the problems of finger rotation and displacement. We first locate the finger midline to be used in correcting the oblique images. We then use a sliding window to determine the Proximal inter phalangeal joint and to further identify the ROI height. Finally, from the corrected image of a certain height, the ROI is obtained through the use of finger edges internal tangents as ROI boundaries. The results prove that our method in a more accurate and effective manner in comparison with the method of [1], and thus enhance the performance of the system.

Image Enhancement In Video Analytics Systems

Recently, video analytics systems are rapidly evolving, and the effectiveness of their work depends primarily on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. This paper presents a new, accurate, and practical method for assessing the improvement of image quality in automatic mode. The method is based on the use of nonlinear transformation function, namely, gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. The technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.

SCS+C Topographic Correction to Enhance SVM Classification Accuracy

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4% which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification90.1%accuracy compared to the uncorrected image86.2%for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification