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International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

作者： Das, Saneev Kumar Bose, Pratik Patra, Kalpita Chakravarty, Sujata Centurion Univ Technol & Management Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9798350345421

With the advent of remote sensing in diverse application areas, many novel devices, algorithms, and numerous such solutions have been developed and proposed to solve real-world problems. In such a scenario, the development of multispectral and hyperspectral cameras has proved to be a boon for the remote sensing society. These cameras work based on the electromagnetic spectrum and allows us to visualize terrains in diverse perspectives. This paper has its prime focus in visualizing all possible band combinations of the collected multispectral imagery dataset. The dataset consists of images that has been acquired using unmanned aerial vehicles acronymized UAVs with mounted multispectral camera having four different bands viz., green, red, red-edge, and near infrared (NIR). The dataset consisting the multispectral imagery is then subjected to the proposed framework in order to visualize diverse band combinations. Initially, the imagery data is processed to generate the orthomosaic map of the entire study area i.e., selected prior to flying of the UAV. Further, the orthomosaic map is tested with different band combinations which can be used by the cartographers or analysts to perform diverse analysis such as identification of healthy vegetation, water absorption rate, edaphic properties, and many such. Also, few challenges that were faced during implementation are provided following which some possible solutions and future research directions are also entailed. The total number of band combinations obtained through the practical realization of the proposed framework included 24 images. Further, the thematic maps generated using individual bands along with MODIS NDVI maps are epitomized. The relevance of each combined image has diverse perspectives to visualize. Certain future directions in identifying the relevant band combinations are also entailed.

关键词： multispectral image processing Orthomosaic Generation Reflectance Calibration MODIS NDVI Band Combinatorics

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A New Method Of multispectral image processing With Camouflage Effect Detection

A New Method Of Multispectral Image Processing With Camoufla...

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Conference on Applied Optics and Photonics (AOPC) - image processing and Analysis

作者： Hu, Jianghua Cui, Guangzhen Qin, Lei PLA Univ Sci & Technol State Key Lab Electromagnet Environm Effects & El Nanjing 210007 Jiangsu Peoples R China Xian Inst Commun Dept Informat Transmiss Xian 710106 Peoples R China

ISBN: (纸本)9781628419009

In order to enhance the observability of multispectral image and improve the accuracy of camouflage effect evaluation based on multispectral photographic. A new method for multispectral image processing has been put forward. In the visible band, more spectral images for image fusion which are based on wavelet transformation respectively are choosed. The image information is enhanced. The visible light and near infrared band images are fused and introduced in three-channel of red, green and blue. The true color image is synthetized. While the detail of visible light image is enhanced, near infrared image information which is more interesting in camouflage evaluation is kept. Finally the fusion image are processed through histogram stretching and correlation method. The image color and luminance difference of each part is enhanced. The target recognition and camouflage effect evaluation is more advantageous. The experimental results proved that the method has a good effect.

关键词： multispectral image processing Camouflage effect evaluation Fusion image wavelet transformation

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Web and MATLAB-Based Platform for UAV Flight Management and multispectral image processing

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SENSORS 2022年第11期22卷 4243-4243页

作者： Aliane, Nourdine Gomez Munoz, Carlos Quiterio Sanchez-Soriano, Javier Univ Europea Madrid Ind & Aerosp Engn Dept Madrid 28670 Spain Univ Francisco Vitoria Escuela Politecn Super Madrid 28223 Spain

The deployment of any UAV application in precision agriculture involves the development of several tasks, such as path planning and route optimization, images acquisition, handling emergencies, and mission validation, to cite a few. UAVs applications are also subject to common constraints, such as weather conditions, zonal restrictions, and so forth. The development of such applications requires the advanced software integration of different utilities, and this situation may frighten and dissuade undertaking projects in the field of precision agriculture. This paper proposes the development of a Web and MATLAB-based application that integrates several services in the same environment. The first group of services deals with UAV mission creation and management. It provides several pieces of flight conditions information, such as weather conditions, the KP index, air navigation maps, or aeronautical information services including notices to Airmen (NOTAM). The second group deals with route planning and converts selected field areas on the map to an UAV optimized route, handling sub-routes for long journeys. The third group deals with multispectral image processing and vegetation indexes calculation and visualizations. From a software development point of view, the app integrates several monolithic and independent programs around the MATLAB Runtime package with an automated and transparent data flow. Its main feature consists in designing a plethora of executable MATLAB programs, especially for the route planning and optimization of UAVs, images processing and vegetation indexes calculations, and running them remotely.

关键词： unmanned aerial vehicle (UAV) precision agriculture (PA) remote sensing (RS) multispectral image processing MATLAB (R) web programming

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A fast construction algorithm for compact singular value decomposition of dual quaternion matrix and its application

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NUMERICAL ALGORITHMS 2025年 1-24页

作者： Wang, Tao Li, Ying Zhang, Mingcui Wei, Musheng Guo, Wenbin Liaocheng Univ Coll Math Sci Liaocheng 252000 Shandong Peoples R China Shanghai Normal Univ Coll Math & Sci Shanghai 200234 Peoples R China

In this paper, we propose the dual complex representation of a dual quaternion matrix and give some important properties. Using the dual complex representation and its properties, we find the relation of the compact singular value decomposition of a dual quaternion matrix and the singular value decomposition of its corresponding dual complex representation matrix. Based on this relation, we propose a fast construction algorithm for the compact singular value decomposition of a dual quaternion matrix and apply it to multispectral image compression, encryption and decryption. Numerical experiments show that, compared with the existing algorithms for the singular value decomposition of a dual quaternion matrix, this fast construction algorithm has great advantages in computational efficiency on the premise of ensuring computational accuracy and a good performance in multispectral image compression, encryption and decryption.

关键词： Fast construction algorithm Dual quaternion matrix Dual complex representation matrix Compact singular value decomposition multispectral image processing

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A Patch-image Based Classification Approach for Detection of Weeds in Sugar Beet Crop

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IEEE ACCESS 2021年 9卷 121698-121715页

作者： Moazzam, S. Imran Khan, Umar S. Qureshi, Waqar S. Tiwana, Mohsin, I Rashid, Nasir Alasmary, Waleed S. Iqbal, Javaid Hamza, Amir Natl Univ Sci & Technol Natl Ctr Robot & Automat NCRA Robot Design & Dev Lab Islamabad 45200 Pakistan Umm Al Qura Univ Dept Comp Sci Mecca 21421 Saudi Arabia

Weeds affects crops health as it shares water and nutrients from the soil, as a result it decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous weedicide spray through aerial spraying units can help save water, weedicide chemical and effect less on human health. Such systems require multi-spectral cues to classify crop, weed, and soil surface. Our focus in this paper is on the detection of weeds in the sugar beet crop, using air-borne multispectral camera sensors, which is considered as an alternative crop to sugarcane to obtain sugar in Pakistan. We developed a new framework for weed identification;a patch-based classification approach as appose to semantic segmentation that is more realistic for real-time intelligent aerial spraying systems. Our approach converts 3-class pixel classification problem into a 2-class crop-weed patch classification problem which in turns improves crop and weed classification accuracy. For classification, we developed a new VGG-Beet convolutional neural network (CNN), which is based on generic VGG16 (visual graphics group) CNN model with 11 convolutional layers. For experiments, we captured a sugar beet dataset with 3-channel multispectral sensor with a ground sampling distance (GSD) of 0.2 cm/pixel and a height of 4 meters. For better comparison, we used two publicly available sugar beet crop aerial imagery datasets, captured using a 5-channel multispectral sensor and a 4-Channel multispectral sensor with a ground sampling distance of 1cm and a height of 10 meters. We observed that patch-based method is more robust to different lighting conditions. To produce low cost weed detection system usage of Agrocam sensor is recommended, for higher accuracy Red Edge and Sequoia multispectral sensors with more channels should be deployed. We observed higher crop-weed accuracy and lower testing time for our patch-based approach as compared to U-Net and Deeplab based semantic segmentation

关键词： Agriculture Sugar industry Semantics Drones Spraying Sensors Convolutional codes Autonomous weed detection drone weed detection deep learning in agriculture multispectral image processing

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The IP-based storage of image information

The IP-based storage of image information

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Conference on image Compression and Encryption Technologies

作者： Fu, XL Xie, CS Liu, ZB Huazhong Univ Sci & Technol Natl Storage Syst Lab Comp Sch Wuhan 430074 Peoples R China

ISBN: (纸本)0819442798

With the fast growth of data in multispectral image processing, the traditional storage architecture was challenged, It is currently being replaced by Storage Area Networks (SAN), which makes storage devices externalized from servers. A SAN is a separate network for storage, isolated from the messaging network and optimized for the movement of data between servers and storage devices. Nowadays, most of current SAN use Fibre Channel to move data between servers and storage devices (FC-SAN), but because of the drawbacks of the FC-SAN: poor interoperability, lack of skilled professional and management tools, high implementation cost and so on, the development and application of FC-SAN was obstructed. In this paper, we introduce an IP-based Storage Area Networks architecture, which has the good qualities of FC-SAN but overcomes the shortcoming of it. The principle is: use IP technology to move data between servers and storage devices, build a SAN with the IP-based network devices (not the FC-based network device), and through the switch, SAN is attached to the LAN(Local Area Network) through multiple access. Especially, These storage devices are acted as commercial NAS devices and PC.

关键词： multispectral image processing Storage Area Networks(SAN) IP-based SAN

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SNAPSHOT multispectral image COMPLETION VIA SELF-DICTIONARY TRANSFORMED TENSOR NUCLEAR NORM MINIMIZATION WITH TOTAL VARIATION

SNAPSHOT MULTISPECTRAL IMAGE COMPLETION VIA SELF-DICTIONARY ...

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IEEE International Conference on image processing (ICIP)

作者： Ozawa, Keisuke Sumiyoshi, Shinichi Sekikawa, Yusuke Uto, Keisuke Yoshida, Yuichi Ambai, Mitsuru DENSO IT Lab Tokyo Japan

ISBN: (纸本)9781665441155

Snapshot multispectral imaging suffers from severely low spatial resolution and degraded signals due to mosaic rearrangement. In order to recover a signal of full bands and full sensor size from a single snapshot, we propose a self-dictionary-transformed tensor nuclear norm and develop a joint optimization with total variation regularization as a convex completion problem. The proposed nuclear norm is designed specifically for the intrinsic structure of snapshot multispectral data, reflects the parsimony of tensors that conventional approaches ignore, as well as incorporates interaxis correlation unlike matrix-based optimization. We show increased accuracy with our self-dictionary throughout simulation experiments and demonstrate quality enhancement in recovering real snapshot multispectral images.

关键词： multispectral image processing tensor decomposition data completion convex optimization

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multispectral co-occurrence with three random variables in dynamic contrast enhanced magnetic resonance imaging of breast cancer

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2008年第10期27卷 1425-1431页

作者： Kale, Mehmet C. Clymer, Bradley D. Koch, Regina M. Heverhagen, Johannes T. Sammet, Steffen Stevens, Robert Knopp, Michael V. Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Ohio State Univ Dept Biomed Engn Columbus OH 43210 USA Ohio State Univ Dept Radiol Columbus OH 43210 USA

Presented is a new computer-aided multispectral image processing method which is used in three spatial dimensions and one spectral dimension where the dynamic, contrast enhanced magnetic resonance parameter maps derived from voxel-wise model-fitting represent the spectral dimension. The method is based on co-occurrence analysis using a 3-D window of observation which introduces an automated identification of suspicious lesions. The co-occurrence analysis defines 21 different statistical features, a subset of which were input to a neural network classifier where the assessments of the voxel-wise majority of a group of radiologist readings were used as the gold standard. The voxel-wise true positive fraction (TPF) and false positive fraction (FPF) results of the computer classifier were statistically indistinguishable from the TPF and FPF results of the readers using a one sample paired t-test. In order to observe the generality of the method, two different groups of studies were used with widely different image acquisition specifications.

关键词： breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) multispectral image processing neural networks statistical co-occurrence analysis

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JU-VNT: a multi-spectral dataset of indoor object recognition using visible, near-infrared and thermal spectrum

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第12期80卷 17807-17826页

作者： Ghosh, Swarnendu Das, Nibaran Sarkar, Priyam Nasipuri, Mita Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 WB India

Detecting objects in natural scenes can be a very challenging task. In several real-life scenarios it is often found that visible spectrum is not ideal for typical computer vision tasks. Going beyond the range of visible light spectrum, such as the near infrared spectrum or the thermal spectrum allows us to capture many unique properties of objects that normally not captured with a normal camera. In this work we propose two multi-spectral dataset with three different spectrum, namely, the visible, near infrared and thermal spectrum. The first dataset is a single object dataset where we have common desk objects of 25 different categories comprising of various materials. The second dataset comprises of all possible combination using these 25 objects taking a pair at a time. The objects are captured from 8 different angles using the three different cameras. The images are registered and cropped and provided along with classification and localization ground truths. Additionally classification benchmarks have been provided using the ResNet, InceptionNet and DenseNet architectures on both the datasets. The dataset would be publicly available from .

关键词： multispectral image processing Thermal image Near infrared image Deep learning

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Geometric Calibration of Lens and Filter Distortions for multispectral Filter-Wheel Cameras

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IEEE TRANSACTIONS ON image processing 2011年第2期20卷 496-505页

作者： Brauers, Johannes Aach, Til Rhein Westfal TH Aachen Inst Imaging & Comp Vis D-52056 Aachen Germany

High-fidelity color image acquisition with a multispectral camera utilizes optical filters to separate the visible electromagnetic spectrum into several passbands. This is often realized with a computer-controlled filter wheel, where each position is equipped with an optical bandpass filter. For each filter wheel position, a grayscale image is acquired and the passbands are finally combined to a multispectral image. However, the different optical properties and non-coplanar alignment of the filters cause image aberrations since the optical path is slightly different for each filter wheel position. As in a normal camera system, the lens causes additional wavelength-dependent image distortions called chromatic aberrations. When transforming the multispectral image with these aberrations into an RGB image, color fringes appear, and the image exhibits a pincushion or barrel distortion. In this paper, we address both the distortions caused by the lens and by the filters. Based on a physical model of the bandpass filters, we show that the aberrations caused by the filters can be modeled by displaced image planes. The lens distortions are modeled by an extended pinhole camera model, which results in a remaining mean calibration error of only 0.07 pixels. Using an absolute calibration target, we then geometrically calibrate each passband and compensate for both lens and filter distortions simultaneously. We show that both types of aberrations can be compensated and present detailed results on the remaining calibration errors.

关键词： Camera calibration chromatic aberration geometric distortion multispectral image processing multispectral imaging model

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