A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

Davide Castelletti; Begüm Demir; Lorenzo Bruzzone

doi:10.1117/12.2069607

15 October 2014 A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

Davide Castelletti, Begüm Demir, Lorenzo Bruzzone

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Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 924412 (2014) https://doi.org/10.1117/12.2069607
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands

Abstract

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ε-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

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Davide Castelletti, Begüm Demir, and Lorenzo Bruzzone "A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924412 (15 October 2014); https://doi.org/10.1117/12.2069607

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KEYWORDS

Statistical analysis

Solid state lighting

Biological research

Remote sensing

Detection and tracking algorithms

LIDAR

Statistical modeling

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