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CLASSIFIER FUSION OF HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA FOR IMPROVEMENT OF LAND COVER CLASSIFCATION
2013
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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This paper presents a new method based on the definition of a Multiple Classifier System on Hyperspectral and LIDAR data. ...
The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. ...
Acknowledgment The authors would like to thank the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the data sets ...
doi:10.5194/isprsarchives-xl-1-w3-97-2013
fatcat:tzr2syre3jdutmhghf3efkvhlq
A Comprehensive Review: Classification Techniques on Hyperspectral Remote Sensing
2019
International Journal of Advanced Trends in Computer Science and Engineering
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The results of this study recommend classification techniques that can be used based on performance parameters. ...
Classification is a big challenge in remote sensing because of many factors that affect its success. ...
Technology that provides detailed spectrum information for individual pixels of images that mostly refers to remote sensing is hyperspectral remote sensing (HRS) [2] . ...
doi:10.30534/ijatcse/2019/3181.52019
fatcat:3kbgewtygra75amchskvcnwopq
Ensemble of Multilayer Perceptrons for Change Detection in Remotely Sensed Images
2014
IEEE Geoscience and Remote Sensing Letters
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Results show that the proposed ensemble technique has an edge over individual base classifiers for change detection in remotely sensed images. ...
Then, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of the base classifiers using different combination rules. ...
sensed images. ...
doi:10.1109/lgrs.2013.2245855
fatcat:g7wijdxhjbehfnvqwgzadnc3ae
Self-Supervised Classification for Planetary Rover Terrain Sensing
2007
2007 IEEE Aerospace Conference
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This paper proposes a self-supervised classification approach to learning the visual appearance of terrain classes which relies on vibration-based sensing of wheel-terrain interaction to identify these ...
Current terrain sensing and path planning approaches can be used to avoid geometric hazards, such as rocks and steep slopes, but are unable to remotely identify and avoid non-geometric hazards, such as ...
CONCLUSIONS In this work, two self-supervised classification approaches were described to create a visual classifier based on terrain classes sensed locally using a vibration-based terrain classifier. ...
doi:10.1109/aero.2007.352693
fatcat:5eo2zfqiifadxojhm53ns6wwhq
CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS
2015
Jurnal Teknologi
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Graphical abstract Abstract Land use and land cover (LU/LC) classification of remotely sensed data is an important field of research by which it is commonly used in remote sensing applications. ...
In this study, the different types of classification techniques were compared using different RGB band combinations for classifying several satellite images of some parts of Selangor, Malaysia. ...
Acknowledgement The authors would like to thank Agency Remote Sensing Malaysia (ARSM) for providing the satellite imagery. ...
doi:10.11113/jt.v74.4838
fatcat:6a3tibmyjfh6phiwycafmmtjua
Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter
2015
Mathematical Problems in Engineering
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remote sensing images. ...
The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods. ...
of VHR remote sensing images. ...
doi:10.1155/2015/182439
fatcat:nmx5c6kidfhd3egtlnp7wh5v4q
Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers
2020
Remote Sensing
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Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. ...
Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this ...
SAM is among one of the most widely used per-pixel-based supervised classification techniques for hyperspectral and multispectral remote sensing images in geological mapping [17] . ...
doi:10.3390/rs12010177
fatcat:vkklfwqj2zdp3gia5eeqnkunle
Remote Sensing Textual Image Classification based on Ensemble Learning
2016
International Journal of Image Graphics and Signal Processing
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Experiments on five UCI data sets and remote sensing image data sets are performed to testify the effectiveness of the proposed method. ...
Remote sensing textual image classification technology has been the hottest topic in the filed of remote sensing. Texture is the most helpful symbol for image classification. ...
Experiments for Remote Sensing Image Data Sets In order to further illustrate the performance of our method on real remote sensing images, we selected some real remote sensing images as training data. ...
doi:10.5815/ijigsp.2016.12.03
fatcat:axda2atyivdp5krwjutbrpahdi
Remote Sensing Textual Image Classification based on Ensemble Learning
2016
International Journal of Image Graphics and Signal Processing
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Experiments on five UCI data sets and remote sensing image data sets are performed to testify the effectiveness of the proposed method. ...
Remote sensing textual image classification technology has been the hottest topic in the filed of remote sensing. Texture is the most helpful symbol for image classification. ...
Experiments for Remote Sensing Image Data Sets In order to further illustrate the performance of our method on real remote sensing images, we selected some real remote sensing images as training data. ...
doi:10.5815/ijigsp.2015.12.03
fatcat:gwywvpll5nhqvb4dgdxa35aiay
A survey of band selection techniques for hyperspectral image classification
2020
Journal of Spectral Imaging
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Hyperspectral images usually contain hundreds of contiguous spectral bands, which can precisely discriminate the various spectrally similar classes. ...
Our purpose is to highlight the progress attained in band selection techniques for hyperspectral image classification and to identify possible avenues for future work, in order to achieve better performance ...
Prabukumar, "A review on graph-based semi-supervised learning methods for hyperspectral image classification", Egypt. J. Remote Sens. Sp. ...
doi:10.1255/jsi.2020.a5
fatcat:cvibjoofbbd6jpu4ij626wigdy
Land-cover Classification and Mapping for Eastern Himalayan State Sikkim
[article]
2010
arXiv
pre-print
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Classifier based on ANN shows satisfactory and accurate result in comparison with the classical method. ...
This paper suggests an improvised k-means and Artificial Neural Network (ANN) classifier for land-cover mapping of Eastern Himalayan state Sikkim. ...
Image classification techniques in remote sensing can be divided into supervised and unsupervised methods based on the involvement of the user during the classification process. ...
arXiv:1003.4087v1
fatcat:jucvsooda5fa7jllr6tecpzbna
A survey of image classification methods and techniques for improving classification performance
2007
International Journal of Remote Sensing
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Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. ...
Remote-sensing classification process Remote-sensing classification is a complex process and requires consideration of many factors. ...
Improving classification performance 849 Downloaded by [207.241.229.243] at 11:53 05 November 2017 ...
doi:10.1080/01431160600746456
fatcat:xs7y7x4bpfhnfn5ahesllpchei
Remote Sensing Satellite Images Classification Using Support Vector Machine and Particle Swarm Optimization
2012
2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications
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With the help of semi supervised learning algorithm and multispectral sensing image the overall performance of PSNR is increased upto 42.98%. ...
The problem of scarcity of multi image supporting for satellite image is handled with the help of multispectral sensing image method. ...
After that the SVM factors are fixed based Remote Sensing Images", the Information Society on the computation time and the processing speed. ...
doi:10.1109/ibica.2012.61
dblp:conf/ibica/SolimanMH12
fatcat:bey2ni63drbzpmnho3geryinue
Weighted decision fusion for supervised and unsupervised hyperspectral image classification
2010
2010 IEEE International Geoscience and Remote Sensing Symposium
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Most decision fusion approaches mainly focus on supervised classifiers as base learner, i.e., all classifiers need training, so the classification results can only be as good as training data. ...
Many studies have been undertaken to develop and analyze the combination of results from different classifiers for a better result than using each individual classifier [1-2]. ...
Although individual classifiers are pixel-based, the final fused classification has a similar result to an object-based classifier [3] ; however, the overall performance using classifier fusion is less ...
doi:10.1109/igarss.2010.5649032
dblp:conf/igarss/YangDM10
fatcat:pvvytezdarebjlht7i7t6olp7e
Assessing the Accuracy of Different Supervised Classification Methods of Satellite Image
2020
Engineering & Technology Review
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Assessing the accuracy of the classification map is an essential area in remote sensing digital image process. ...
Seven land use/land cover categories were identified on the image data and appropriate information classes were also assigned using region of interest. ...
But (Souza et al 2013) used ENVI 4.7 to performed supervised image classification. The choice of ArcGIS was based on its ability for vector operations. ...
doi:10.47285/etr.v1i1.34
fatcat:bx4ifactubfb7dxstxw24uu3pe
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