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On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images

G. Tochon, J.B. Féret, S. Valero, R.E. Martin, D.E. Knapp, P. Salembier, J. Chanussot, G.P. Asner
2015 Remote Sensing of Environment  
In this paper, we propose a method for hyperspectral image segmentation, based on the Binary Partition Tree (BPT) algorithm, and we apply it to two sites located in Hawaiian and Panamean tropical rainforests  ...  The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications, such as forest inventories or conservation and management of ecosystems  ...  The third approach was based on the mean shift clustering (Comaniciu & Meer, 2002) of a RGB representation of the hyperspectral data.  ... 
doi:10.1016/j.rse.2014.12.020 fatcat:imcoh6a6u5bqxbjaguuztif6be

Multispectral Satellite Imagery Classification Using a Fuzzy Decision Tree

Sergey Stankevich, Vitaly Levashenko, Elena Zaitseva
2014 Communications - Scientific Letters of the University of Zilina  
sensing application-oriented A new algorithm for remote sensing multi- and hyperspectral informativity metrics entering into the algorithms. imagery classification based on a fuzzy decision  ...  to carry out this study and for the constructive performance practical analysis of remote sensing hyperspectral discussion on the results obtained.  ... 
doi:10.26552/com.c.2014.1.109-113 fatcat:sthlynovyrapvneflaxuikgwa4

Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

Y. Tarabalka, J.A. Benediktsson, J. Chanussot
2009 IEEE Transactions on Geoscience and Remote Sensing  
The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images.  ...  A new spectral-spatial classification scheme for hyperspectral images is proposed.  ...  Landgrebe from Purdue University, USA, for providing the hyperspectral data. The authors would also like to thank I. Kåsen, T. V. Haavardsholm, and T.  ... 
doi:10.1109/tgrs.2009.2016214 fatcat:6he22digvnd6tmim5nvrbmsgxe

Chronological Advancement in Image Processing from Lime Stone Mofits to Superpixel Classification

Er. Sumit Kaur, R. K. Bansal
2015 Journal of Computer Science  
While extracting the information from the remote sensed images the major issues that affect the accuracy of the classification is the presence of mixed pixels (reflecting more than one spectral signature  ...  To get the information from the areas and objects which are not possible to be physically contact directly remote sensing image processing is used.  ...  The data present in this article is not published anywhere else. This article is approved by corresponding author that there is no ethical issue in it.  ... 
doi:10.3844/jcssp.2015.1060.1074 fatcat:c7wkodloe5hltpnaoxh2xkvbte

Automatic Image Registration Through Image Segmentation and SIFT

Hernâni Goncalves, Luís Corte-Real, José A. Goncalves
2011 IEEE Transactions on Geoscience and Remote Sensing  
Automatic image registration (AIR) is still a present challenge for the remote sensing community.  ...  In this paper, a new AIR method is proposed, based on the combination of image segmentation and SIFT, complemented by a robust procedure of outlier removal.  ...  A similar performance with the k-means clustering technique for the four pairs of images was found.  ... 
doi:10.1109/tgrs.2011.2109389 fatcat:agdhbcw5qvdpfjmjx5mfatq2gu

Neuro-fuzzy Based Analysis of Hyperspectral Imagery

Fang Qiu
2008 Photogrammetric Engineering and Remote Sensing  
A geovisualization tool was developed to facilitate knowledge discovery and understanding of the hyperspectral image. A case study was conducted using a Hyperion image.  ...  A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization (GFLVQ), was developed based on the synergy of a neural network and a fuzzy system.  ...  Qi Li helped with the migration of the system from the UNIX system to the Windows ® environment.  ... 
doi:10.14358/pers.74.10.1235 fatcat:x4nd5pwrszhtjpldew2gcwrgmy

Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles

Pedram Ghamisi, Jon Atli Benediktsson, Gabriele Cavallaro, Antonio Plaza
2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Without any doubt, classification (or mapping) can be considered as the backbone of most image interpretation in remote sensing.  ...  However, most of the existing classification techniques have been developed for the analysis of multispectral images, and consequently, they are not usually efficient for the classification of hyperspectral  ...  Gamba from the University of Pavia, Italy, for providing the ROSIS data and corresponding reference information and Dr. P. Marpu for his contributions.  ... 
doi:10.1109/jstars.2014.2298876 fatcat:nxj4xswdlvc3bb47wujib2lahi

Recent Advances in Forest Insect Pests and Diseases Monitoring Using UAV-Based Data: A Systematic Review

André Duarte, Nuno Borralho, Pedro Cabral, Mário Caetano
2022 Forests  
The purpose of this review is to summarize recent contributions and to identify knowledge gaps in UAV remote sensing for FIPD monitoring.  ...  These machines provide flexibility, cost efficiency, and a high temporal and spatial resolution of remotely sensed data.  ...  Acknowledgments: The authors would like to thank Cindy Santos, Luís Acevedo-Muñoz, João Rocha and Sérgio Fabres, for all their valuable comments and support.  ... 
doi:10.3390/f13060911 doaj:a077b5a200d744cbb9b504c1872e7739 fatcat:wzcqjv6rdvb5bm7nwfupozpxxm

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
2017 IEEE Geoscience and Remote Sensing Magazine  
In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously  ...  There are controversial opinions in the remote sensing community.  ...  The output of the SAE is used as a feature in the final step for k-nearest neighbor clustering of superpixels. Zhang et al.  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

Damage assessment framework for landslide disaster based on very high-resolution images

Bo Sun, Qihua Xu, Jun He, Zhen Liu, Ying Wang, Fengxiang Ge
2016 Journal of Applied Remote Sensing  
Based on an analysis of very high-resolution remote-sensing images, we propose an automatic building damage assessment framework for rainfall-or earthquake-induced landslide disasters.  ...  Damage assessment framework for landslide disaster based on very high-resolution images," Abstract.  ...  Acknowledgments This research was primarily supported by the National Natural Science Foundation of China  ... 
doi:10.1117/1.jrs.10.025027 fatcat:qelow3lu75glphm7s2rmjptdgq

Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Bini Darwin, Pamela Dharmaraj, Shajin Prince, Daniela Elena Popescu, Duraisamy Jude Hemanth
2021 Agronomy  
Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation.  ...  The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used.  ...  The multispectral and hyperspectral images acquired through remote sensing were used for monitoring seasonally variable crop and soil status features such as crop diseases, crop biomass, the nitrogen content  ... 
doi:10.3390/agronomy11040646 fatcat:n3ru7ggspvgixlcu24meshbax4

Hyperspectral Image Representation and Processing With Binary Partition Trees

S. Valero, P. Salembier, J. Chanussot
2013 IEEE Transactions on Image Processing  
Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics.  ...  This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT).  ...  Fauvel for his support in performing the spectral-spatial classification comparison.  ... 
doi:10.1109/tip.2012.2231687 pmid:23221824 fatcat:lfufcjxdwrfu5biwlmpfssdkyi

Reducing the Complexity of Genetic Fuzzy Classifiers in Highly-Dimensional Classification Problems

Dimitris G. Stavrakoudis, Georgia N. Galidaki, Ioannis Z. Gitas, John B. Theocharis
2012 International Journal of Computational Intelligence Systems  
Comparative results in a hyperspectral remote sensing classification as well as in 12 real-world classification datasets indicate the effectiveness of the proposed methodology in generating high-performing  ...  The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the  ...  Application in Hyperspectral Remote Sensing Classification Remote sensing classification from hyperspectral satellite images is an arduous task, because of the large number of features involved and the  ... 
doi:10.1080/18756891.2012.685290 fatcat:d4rcvwjw3vh6blavulq5o33zfq

Recent Advances in Unmanned Aerial Vehicles Forest Remote Sensing—A Systematic Review. Part II: Research Applications

Riccardo Dainelli, Piero Toscano, Salvatore Filippo Di Gennaro, Alessandro Matese
2021 Forests  
acquiring tree spectral signature especially for pest and diseases detection, (2) automatic processes for image analysis are poorly flexible or based on proprietary software at the expense of flexible  ...  Due to the progress in platforms and sensors and the opening of the dedicated market, unmanned aerial vehicle–remote sensing (UAV–RS) is improving its key role in the forestry sector as a tool for sustainable  ...  Basing on a rigorous review, results are elaborated to address the pivotal research questions for the set of studies, previously clustered by six forestry topics.  ... 
doi:10.3390/f12040397 fatcat:6mtlejuku5c3xbx2eq7inpdjse

Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images with Label Noise

Jian Kang, Ruben Fernandez-Beltran, Xudong Kang, Jingen Ni, Antonio J Plaza
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks.  ...  Our experiments, conducted on two benchmark RS datasets, validate the effectiveness of the proposed approach on three different RS scene interpretation tasks, including classification, clustering, and  ...  INTRODUCTION W ITH the rapid development of satellite sensors, remote sensing (RS) has entered the big data era.  ... 
doi:10.1109/jstars.2021.3056661 fatcat:h6vv4f7rfrhtpjchpqn4u63poq
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