Supervised change detection in VHR images using contextual information and support vector machines

Volpi, Michele; Tuia, Devis; Bovolo, Francesca; Kanevski, Mikhaïl; Bruzzone, Lorenzo

doi:10.1016/j.jag.2011.10.013

Cited by 229 publications

(131 citation statements)

References 26 publications

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“…Under supervised frameworks every training dataset must be adequately exploited in order to describe compactly and adequately each class. To this end, training datasets may consist of a combination of spectral bands, morphological filters [Lefevre et al, 2007], texture [Volpi et al, 2013], point descriptors [Wang et al, 2013], gradient orientation [Benedek et al, 2012], etc. * These authors contributed equally to this work.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

Papadomanolaki¹,

Vakalopoulou²,

Zagoruyko³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

Papadomanolaki¹,

Vakalopoulou²,

Zagoruyko³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…(2) SVM, which is a nonparametric supervised classifier relying on Vapnik's statistical learning theory [17], is chosen owing to its intrinsic robustness to high-dimensional datasets and to ill-posed problems. It possesses the advantages of superior generalization ability and insensitive value and is suitable for solving high-dimensional, small-sample, non-linear model classification and return problems.…”

Section: Change Detection With Elmentioning

confidence: 99%

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Wang

Liu

et al. 2018

Remote Sensing

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Abstract:To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

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“…Besides, preparing training samples for supervised classifiers is a complex, time consuming and expensive process. To avoid the bad effects of misclassified samples and consider the temporal correlation, some supervised methods stack multi-temporal images together and take into account the dependence between two images of the same area [21,22]. The general idea is to characterize pixels or objects by stacking the feature vectors of two images.…”

Section: Introductionmentioning

confidence: 99%

“…Then, land-cover transition classifiers are carried out to recognize specific transitions provided by training samples. In [22], Volpi et al adopted the nonlinear SVMs to cope with the high intra-class variability, which achieved high detection accuracy in very high geometrical resolution images. In addition, to reduce the tedious workload of labelling, some semi-supervised change detection methods are proposed.…”

Section: Introductionmentioning

confidence: 99%

Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields

Yang

Hua

et al. 2017

Remote Sensing

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Abstract:Change detection has been widely used in remote sensing, such as for disaster assessment and urban expansion detection. Although it is convenient to use unsupervised methods to detect changes from multi-temporal images, the results could be further improved. In supervised methods, heavy data labelling tasks are needed, and the sample annotation process with real categories is tedious and costly. To relieve the burden of labelling and to obtain satisfactory results, we propose an interactive change detection framework based on active learning and Markov random field (MRF). More specifically, a limited number of representative objects are found in an unsupervised way at the beginning. Then, the very limited samples are labelled as "change" or "no change" to train a simple binary classification model, i.e., a Gaussian process model. By using this model, we then select and label the most informative samples by "the easiest" sample selection strategy to update the former weak classification model until the detection results do not change notably. Finally, the maximum a posteriori (MAP) change detection is efficiently computed via the min-cut-based integer optimization algorithm. The time consuming and laborious manual labelling process can be reduced substantially, and a desirable detection result can be obtained. The experiments on several WorldView-2 images demonstrate the effectiveness of the proposed method.

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Supervised change detection in VHR images using contextual information and support vector machines

Cited by 229 publications

References 26 publications

Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

Benchmarking Deep Learning Frameworks for the Classification of Very High Resolution Satellite Multispectral Data

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Change Detection Using High Resolution Remote Sensing Images Based on Active Learning and Markov Random Fields

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