Results 151 to 160 of about 2,046 (182)

Monitoring radioactive contamination by hyperspectral lidar

SPIE Proceedings, 2015
There are already significant amounts of hazardous radioactive substances in the world. It, potentially, leads to a major damage and contamination of large areas. Laser sensing can serve as a highly effective method of searching and monitoring of radioactive contamination.
A. S. Grishkanich, V. G. Bespalov, S. K. Vasiev, A. S. Gusarov, S. V. Kascheev, V. V. Elizarov, A. P. Zhevlakov   +6 more
openaire   +1 more source

Deep fusion of hyperspectral and LiDAR data for thematic classification

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Recently, the fusion of hyperspectral and light detection and ranging (LiDAR) data has obtained a great attention in the remote sensing community. In this paper, we propose a new feature fusion framework using deep neural network (DNN). The proposed framework employs a novel 3D convolutional neural network (CNN) to extract the spectral-spatial features
Yushi Chen, Chunyang Li, Pedram Ghamisi, Chunyu Shi, Yanfeng Gu   +4 more
openaire   +1 more source

Fusion of hyperspectral and LiDAR data in classification of urban areas

2014 IEEE Geoscience and Remote Sensing Symposium, 2014
In this paper, the fusion of hyperspectral and Li-DAR data is taken into account in order to develop a new classification framework for the accurate analysis of urban areas. In this method, an attribute profile is considered in order to model the spatial information of LiDAR and hyper-spectral data. In parallel, in order to reduce the redundancy of the
Pedram Ghamisi, Jón Atli Benediktsson, Stuart R. Phinn   +2 more
openaire   +4 more sources

Intrinsic Scene Properties From Hyperspectral Images and LiDAR

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019
In this paper, a novel reflectance model is proposed to recover intrinsic images from remote sensing hyperspectral images (HSIs). Intrinsic image recovery is a well-known challenging and underconstrained problem in computer vision and it becomes even more severely ill posed for HSIs.
Xudong Jin, Yanfeng Gu
openaire   +1 more source

Fusion of Hyperspectral and LiDAR Data With a Novel Ensemble Classifier

IEEE Geoscience and Remote Sensing Letters, 2018
Due to the development of sensors and data acquisition technology, the fusion of features from multiple sensors is a very hot topic. In this letter, the use of morphological features to fuse a hyperspectral (HS) image and a light detection and ranging (LiDAR)-derived digital surface model (DSM) is exploited via an ensemble classifier. In each iteration,
Junshi Xia, Naoto Yokoya, Akira Iwasaki
openaire   +2 more sources

Hyperspectral tree species classification with an aid of lidar data

2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014
Classification of tree species is one of the most important applications in remote sensing. A methodology to classify tree species using hyperspectral and LiDAR data is proposed. The data processing consists of shadow correction, individual tree crown delineation, classification by support vector machine (SVM) and postprocessing by a smoothing filter ...
Tomohiro Matsuki, Naoto Yokoya, Akira Iwasaki   +2 more
openaire   +2 more sources

Collaborative Contrastive Learning for Hyperspectral and LiDAR Classification

IEEE Transactions on Geoscience and Remote Sensing, 2023
Sen Jia 0001, Xi Zhou, Shuguo Jiang, Ruyan He   +3 more
openaire   +1 more source

Improved atmospheric compensation of hyperspectral imagery using LIDAR

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
Atmospheric compensation of hyperspectral data is important to a number of critical applications such as spectral unmixing and material identification. Current methods perform well at mitigating effects due to sunlight, skylight, and upwelled radiance, but these methods have no information about the geometric properties of the area being imaged.
Joshua B. Broadwater, Amit Banerjee
openaire   +2 more sources

Multimodal Transformer Network for Hyperspectral and LiDAR Classification

IEEE Transactions on Geoscience and Remote Sensing, 2023
Yiyan Zhang, Shufang Xu, Danfeng Hong, Hongmin Gao 0001, Chenkai Zhang, Meiqiao Bi, Chenming Li   +6 more
openaire   +1 more source

Progressive Semantic Enhancement Network for Hyperspectral and LiDAR Classification

IEEE Transactions on Neural Networks and Learning Systems
The joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data is gaining attention for its improved classification accuracy. However, effectively integrating the rich spectral information of HSI and the elevation features of LiDAR has remained a challenge in multimodal fusion.
Xiyou Fu, Xi Zhou, Yawen Fu, Pan Liu, Sen Jia 0001   +4 more
openaire   +3 more sources