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A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation

Vinayaraj Poliyapram, Weimin Wang, Ryosuke Nakamura
2019 Remote Sensing  
3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless,
more » ... ently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore, this paper proposes a novel deep learning-based end-to-end Point-wise LiDAR and Image Multimodal Fusion Network (PMNet) for 3D segmentation of aerial point cloud by fusing aerial image features. PMNet respects basic characteristics of point cloud such as unordered, irregular format and permutation invariance. Notably, multi-view 3D scanned data can also be trained using PMNet since it considers aerial point cloud as a fully 3D representation. The proposed method was applied on two datasets (1) collected from the urban area of Osaka, Japan and (2) from the University of Houston campus, USA and its neighborhood. The quantitative and qualitative evaluation shows that PMNet outperforms other models which use non-fusion and multimodal fusion (observational-level fusion and feature-level fusion) strategies. In addition, the paper demonstrates the improved performance of the proposed model (PMNet) by over-sampling/augmenting the medium and minor classes in order to address the class-imbalance issues.
doi:10.3390/rs11242961 fatcat:2h2slmdyo5gkpn4ay6gahrvd5a

Implementation of Algorithm for Satellite-Derived Bathymetry using Open Source GIS and Evaluation for Tsunami Simulation

Vinayaraj Poliyapram, Venkatesh Raghavan, Markus Metz, Luca Delucchi, Shinji Masumoto
2017 ISPRS International Journal of Geo-Information  
Accurate and high resolution bathymetric data is a necessity for a wide range of coastal oceanographic research topics. Active sensing methods, such as ship-based soundings and Light Detection and Ranging (LiDAR), are expensive and time consuming solutions. Therefore, the significance of Satellite-Derived Bathymetry (SDB) has increased in the last ten years due to the availability of multi-constellation, multi-temporal, and multi-resolution remote sensing data as Open Data. Effective SDB
more » ... hms have been proposed by many authors, but there is no ready-to-use software module available in the Geographical Information System (GIS) environment as yet. Hence, this study implements a Geographically Weighted Regression (GWR) based SDB workflow as a Geographic Resources Analysis Support System (GRASS) GIS module (i.image.bathymetry). Several case studies were carried out to examine the performance of the module in multi-constellation and multi-resolution satellite imageries for different study areas. The results indicate a strong correlation between SDB and reference depth. For instance, case study 1 (Puerto Rico, Northeastern Caribbean Sea) has shown an coefficient of determination (R 2 ) of 0.98 and an Root Mean Square Error (RMSE) of 0.61 m, case study 2 (Iwate, Japan) has shown an R 2 of 0.94 and an RMSE of 1.50 m, and case study 3 (Miyagi, Japan) has shown an R 2 of 0.93 and an RMSE of 1.65 m. The reference depths were acquired by using LiDAR for case study 1 and an echo-sounder for case studies 2 and 3. Further, the estimated SDB has been used as one of the inputs for the Australian National University and Geoscience Australia (ANUGA) tsunami simulation model. The tsunami simulation results also show close agreement with post-tsunami survey data. The i.mage.bathymetry module developed as a part of this study is made available as an extension for the Open Source GRASS GIS to facilitate wide use and future improvements. research. The highly dynamic nature of near-shore regions leads to frequent changes in bathymetry that are required to be monitored at periodic intervals, and, hence, the survey should be carried out repetitively, which is almost not practical. Remote sensing is considered an alternative for near-shore bathymetry estimation since a large number of multi-constellation, multi-spectral, and multi-spatial satellite data is available as Open Data. Therefore, near-shore bathymetry based on optical remote sensing has become a cost-effective alternative to Sound Navigation and Ranging (SoNAR) and Light Detection and Ranging (LiDAR) surveys. In order to supplement field based approaches, several optical remote sensing methods have been proposed [1] [2] [3] [4] [5] . Satellite-Derived Bathymetry (SDB) models have been purported to retrieve coastal sea bottom reflectance from satellite imagery and effectively utilize this information to generate coastal bathymetry. Researchers have investigated SDB algorithms over the last 30 years and proposed estimation methods falling into categories such as spectral rationing [1, 6] and radiative transfer models [7] [8] [9] . In case of radiative transfer, single spectral band and multispectral band models have been proposed. The single band algorithms assume a constant attenuation coefficient and homogeneous bottom type [8, 10, 11] . Reliable SDB is possible when the water is clear and when water quality and bottom types are homogeneous. When such conditions are satisfied, single band water depth models can provide a reasonable estimate of depth. Nonetheless, coastal water environments rarely offer such ideal conditions. Therefore, radiative transfer models using linear regression of multispectral bands [7, 9, 12] have yielded good results. In order to improve the efficacy of multispectral models in SDB estimation, many statistical approaches have been adopted [13] .
doi:10.3390/ijgi6030089 fatcat:ututigdin5gf7eiylkfpwh6pfe

Transfer learning with CNNs for Segmentation of PALSAR-2 Power Decomposition Components

Poliyapram Vinayaraj, Ryu Sugimoto, Ryosuke Nakamura, Yoshio Yamaguchi
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
(Corresponding author: Poliyapram Vinayaraj.)  ...  Poliyapram Vinayaraj was with the Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan.  ...  Poliyapram Vinayaraj received the master's degree in geoinformatics from Mangalore University, Konaje, India, in 2009, the Ph.D. degree in geoinformatics from Osaka City University, Osaka, Japan, in 2017  ... 
doi:10.1109/jstars.2020.3031020 fatcat:5cggvrxnvna7tklgnjuqvjyuly

Canopy Averaged Chlorophyll Content Prediction of Pear Trees using Convolutional Auto-Encoder on Hyperspectral Data

Subir Paul, Poliyapram Vinayaraj, Nevrez Imamoglu, Kuniaki Uto, Ryosuke Nakamura, Nagesh Kumar D
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Chlorophyll content is one of the essential parameters to assess the growth process of the fruit trees. This present study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional autoencoder (CAE) features of hyperspectral (HS) data. This study also demonstrated the inspection of anomaly among the trees by employing multidimensional scaling on the CAE features and detected outlier trees prior to fit nonlinear regression models. These
more » ... utlier trees were excluded from the further experiments that helped in improving the prediction performance of CACC. Gaussian process regression (GPR) and support vector regression (SVR) techniques were investigated as nonlinear regression models and used for prediction of CACC. The CAE features were proven to be providing better prediction of CACC when compared with the direct use of HS bands or vegetation indices as predictors. The CACC prediction performance was improved with the exclusion of the outlier trees during training of the regression models. It was evident from the experiments that GPR could predict the CACC with better accuracy compared to SVR. In addition, the reliability of the tree canopy masks, which were utilized for averaging the features' values for a particular tree, was also evaluated. Index Terms-Canopy averaged chlorophyll content (CACC), convolutional autoencoder (CAE), deep learning, Gaussian process regression (GPR), hyperspectral (HS) data, pear orchard.
doi:10.1109/jstars.2020.2983000 fatcat:zwkvycmg6jezthg3kpu7kbvnjm

Contents of Geoinformatics Vol.27, Instructions to Contributors (Revised)

using GRASS GIS Python scripting and R Poliyapram Vinayaraj, Venkatesh Raghavan, Luca Delucchi, Tatsuya Nemoto and Shinji Masumoto -Collection and analysis of geological information by diving surveys  ...  Vinayaraj Xuan Luan Truong Go Yonezawa -Crop monitoring using time-series MODIS data: Application validation for Mekong Delta region using free and open source software and open geospatial consortium  ... 
doi:10.6010/geoinformatics.27.4_191 fatcat:l5lxujzok5e4zleywpx6wzrnam

Report of the 1st Council Meeting in 2016, Report of the Geoinforum-2016, 2016 Society Awards, FAQ on Shift of GEOINFORMATICS to full online journal, Revision of the Contribution Rules and the DOI format
2016年度第1回評議員会報告、日本情報地質学会第27回総会・講演会(Geoinforum-2016)報告、2016年度学会賞、学会誌『情報地質』の完全電子化に関するFAQ、投稿規定の改訂とDOI の書式変更について

DEM Pixel based and object based fuzzy LULC classification using GRASS GIS and RapidEye imagery of Lao Cai area, Vietnam Thi Hang Do, Venkatesh Raghavan, Poliyapram Vinayaraj Osaka City Univ. ,  ... 
doi:10.6010/geoinformatics.27.3_149 fatcat:m52xtujftndb7avkhlqgisxvgu


Vinayaraj, Venkatesh Raghavan, Shinji Masumoto and Go Yonezawa -Improvement of the slope gradation image Aiming for disaster mitigation Makoto Inoue -GIS・Web-GIS Development of a viewer of the Seamless  ...  mineral mapping Nguyen Tien Hoang and Katsuaki Koike -Extrapolating near-shore depth using geographically weighted regression of multi-spectral satellite images with consideration of bottom class types Poliyapram  ... 
doi:10.6010/geoinformatics.26.4_169 fatcat:lo5g5bzjjnd4dagakzcoes7lmi


Thi Hang Do・Venkatesh Raghavan・Poliyapram Vinayaraj・Xuan Luan Truong・Go Yonezawa 104 Crop monitoring using time-series MODIS data: Application validation for Mekong Delta region in South Vietnam ......  ... 
doi:10.6010/geoinformatics.27.2_27 fatcat:hr4rnopnt5dv3cefjdzjkdacfq

Introduction of special issue commemorating the 30th anniversary of Japan Society of Geoinformatics

奨励賞 ・ Thi An Tran(Osaka City Univ.)Investigation of algorithm for fusion of optical stereo and InSAR derived global DEM data (Thi An Tran, Venkatesh Raghavan, Shinji Masumoto, Poliyapram Vinayaraj and  ... 
doi:10.6010/geoinformatics.30.4_141 fatcat:cdr2h7ifgndnpn6db5pqwptgfa

Performance Analysis of MongoDB Vs. PostGIS/PostGreSQL Databases For Line Intersection and Point Containment Spatial Queries

Agarwal, Sarthak; Rajan, KS
The previous study (Vinayaraj et al., 2014) reveals that short wave infrared band is better to correct the radiance than near infrared band.  ...  MongoDB PostGIS Non- Index Index Non- Index Index 21 1 1 1.546 9.721 1875 18 20 88.695 48.461 195691 185 190 13364.596 1963.123 < (1000*1000) 4093 3140 >1500000 172048 Poliyapram  ... 
doi:10.7275/r5mg7mqg fatcat:q6mayzeamrbzffr63ajyfduyga

Acknowledgement to Reviewers of Remote Sensing in 2019

Remote Sensing Editorial Office
2020 Remote Sensing  
Villano, Michelangelo Villarreal, Miguel Villarreal-Guerrero, Federico Villarroel, Cristian Villegas, Dolors Villiger, Arturo Vinayaraj, Poliyapram Vincent, Gregoire Vincent, Ron Vincenzo, Levizzani  ... 
doi:10.3390/rs12020327 fatcat:yhqczynu25d2jcnyl55zj3wpqa

Acknowledgment to Reviewers of Sensors in 2020

Sensors Editorial Office Sensors Editorial Office
2021 Sensors  
, Poliyapram Vicent, José F.  ...  Villordo Jimenez, Iclia Vialetto, Giulio Viman, Liviu Viassone, Milena Vimarlund, Vivian Vibell, Jonas Vimieiro, Claysson Bruno Vicarelli, Leonardo Vinayagam, Arangarajan Vicencio, Rodrigo Barraza Vinayaraj  ... 
doi:10.3390/s21030854 pmid:33525311 fatcat:nstzo7kmhbhabjy72svfsbhhky

Table of Contents

2020 IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium  
Vinayaraj, AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL), Japan; Ryosuke Nakamura, National Institute of Advanced Industrial Science and Technology (AIST), Japan  ...  LIDAR POINT CLOUD WITH FUSED ..................................................2655 ENCODER-DECODER NETWORKSWeimin Wang, National Institute of Advanced Industrial Science and Technology (AIST), Japan; Poliyapram  ... 
doi:10.1109/igarss39084.2020.9323828 fatcat:6aittajt35gufeaugcmemu5cya