2021 2nd International Conference on Range Technology (ICORT)最新文献_第5页

Versatile Radio-Frequency Flight Test Recorder 多功能射频飞行测试记录仪

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581923

F. Sandoz, Philippe Klaeyle

引用次数: 0

Substitution Permutation Network based Lightweight Ciphers with Improved Substitution Layers for Secure IoT Applications 基于替换置换网络的轻量级密码，改进了安全物联网应用的替换层

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581374

Ruby Mishra, S. Dutta, M. Okade, K. Mahapatra

引用次数: 2

A Novel Approach for Change Detection Analysis of Land Cover from Multispectral FCC Optical Image using Machine Learning 基于机器学习的多光谱FCC光学图像土地覆盖变化检测分析新方法

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9582057

Khyati K. Patel, Manan Jain, Manish I. Patel, Ruchi Gajjar

{"title":"A Novel Approach for Change Detection Analysis of Land Cover from Multispectral FCC Optical Image using Machine Learning","authors":"Khyati K. Patel, Manan Jain, Manish I. Patel, Ruchi Gajjar","doi":"10.1109/ICORT52730.2021.9582057","DOIUrl":"https://doi.org/10.1109/ICORT52730.2021.9582057","url":null,"abstract":"Land covers refers to the physical land types such as vegetation, water, urban area, roads, and many more according to the geographical region. With the rapid change in land-use patterns, the land covers are varying drastically which requires immediate attention to have an eye at the impact of the land use planning and environmental changes is on the right track, or it needs to be modified. Hence utilizing the advancements in remote sensing technology for analyzing Land Use Land Cover (L ULC) classification maps using satellite images of the geographical region plays an important role in analyzing the present scenario of land covers. This paper proposes a novel approach for change detection analysis using the classification maps generated using Machine Learning (ML) classification techniques on a particular geographical region surrounding Nirma University, Ahmedabad, India. The highest classification accuracy of 98.48% was achieved using Support Vector Machine (SVM) for Near Infrared (NIR) band False Colour Composite (FCC) image obtained from Sentinel 2.","PeriodicalId":344816,"journal":{"name":"2021 2nd International Conference on Range Technology (ICORT)","volume":"44 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132401886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Ground S Band Hybrid Active Electronically Scanned Array: Concept, Design and Performance 地面S波段混合有源电子扫描阵列:概念、设计和性能

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581364

C. Melle, Gerard Kipfer, A. Lohou, A. Karas, B. Lesur, R. Contreres

引用次数: 0

3D Separable Convolution based Super-Resolution of Hyperspectral Images using CNN 基于CNN的三维可分离卷积的高光谱图像超分辨率研究

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581604

K. G. Prasad, S. Deepak, D. Patra

{"title":"3D Separable Convolution based Super-Resolution of Hyperspectral Images using CNN","authors":"K. G. Prasad, S. Deepak, D. Patra","doi":"10.1109/ICORT52730.2021.9581604","DOIUrl":"https://doi.org/10.1109/ICORT52730.2021.9581604","url":null,"abstract":"Recently CNN based Super-Resolution methods have seen rise in popularity in solving the problem ill posed problem of Hyperspectral (HS) Image super resolution. One major factor for this popularity is that CNN based super-resolution methods have been developed to improve the performance and eliminate spectral distortion occurred due to sparse coding based methods which involve fusion of HS image and normal RGB image. Even though these Deep Learning based methods have outperformed the classic sparse coding methods for HS image super resolution, they lag behind in proper exploration of spectral features of the image and at the same time the number of training parameters used are very high, which eventually costs valuable time and computational resources. In order to eliminate these drawbacks, in this paper we propose a 3D Separable Convolution method to address two major tasks at hand. First is to reduce the number of parameters in the network and efficiently reuse the parameters for reducing the training time and resources, which is achieved by employing the 3D Separable Networks comprising of separable filters inside the Deep Feature Extraction subnetwork whose operational analysis is elaborately explained in the upcoming sections. Second, to efficiently use the strong correlation between spectral and spatial features and of the HS image and is performed by the 2D network which makes use of the feature maps obtained by the 3D Separable network to spatially convolve along the feature dimensions of the network extracted by the 3D filters. The experimental results show that the proposed model has achieved reduction in number of training parameters from 19lakhs to 17lakhs compared to the State-of-the-Art (SOTA) method. The proposed method has outperformed the SOTA methods with 5dB improvement in PSNR along with 0.9929 SSIM and 1.668°SAM.","PeriodicalId":344816,"journal":{"name":"2021 2nd International Conference on Range Technology (ICORT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130239884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Manmade structure segmentation based on morphological operation from high resolution PAN image 基于形态学操作的高分辨率PAN图像人工结构分割

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581563

M. P. Singh, V. Gayathri, D. Chaudhuri

引用次数: 0

Design and Development Aspects of Klystrons 速调管的设计与开发

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581565

A. K. Bandyopadhyay, D. Pal, R. Meena, B. Kumar, V. Rawat, D. Kant, A. Nagaraju

引用次数: 0

Design of Wide Band Dielectric Resonator Antenna for X-Band and Lower Ku-Band Applications x波段和低ku波段宽带介质谐振器天线的设计

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581712

Subham Banerjee, Md Sujauddin Ahmmed, A. K. Ray, Pravakar Mallick, Raghvendra Kumar Chaudhury

引用次数: 2

Signal Processing Application for Artillery Measurements using Fixed Head Doppler Radar 固定头多普勒雷达在火炮测量中的信号处理应用

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581668

Aveek Dutta, Shruti Pandey, S. Padhy

引用次数: 0

A Novel Approach for Urban Unsupervised Segmentation Classification in SAR Polarimetry SAR偏振中城市无监督分割分类的新方法

2021 2nd International Conference on Range Technology (ICORT) Pub Date : 2021-08-05 DOI: 10.1109/ICORT52730.2021.9581380

Soumydip Sarkar, Tamesh Halder, Vivek Poddar, R. Gayen, Arundhati M. Ray, D. Chakravarty

引用次数: 0