Semantic Annotation of High-Resolution Remote Sensing Images via Gaussian Process Multi-Instance Multilabel Learning

Chen, Keming; Jian, Ping; Guo, Jian'en; Zhang, Daobing

doi:10.1109/lgrs.2012.2237502

Cited by 23 publications

(17 citation statements)

References 10 publications

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“…The GPC method was originally introduced in the field of remote sensing in [26], where very good capabilities for land cover classification from multi/hyperspectral imagery were illustrated. Since then, GPC has been widely used in practice and extended to many settings: hyperspectral image classification [27], semantic annotation of high-resolution remote sensing images [28], change detection problems with semisupervised GPC [29], or classification of images with the help of user's intervention in active learning schemes [30], [31]. Unfortunately, like any other kernel method, its computational cost is very large.…”

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confidence: 99%

Remote Sensing Image Classification With Large-Scale Gaussian Processes

Morales-Álvarez

Pérez-Suay

Molina

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.

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confidence: 99%

Remote Sensing Image Classification With Large-Scale Gaussian Processes

Morales-Álvarez

Pérez-Suay

Molina

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…In the end, MSC predicts the labels of the query image via linear combination of label vectors of labeled images weighted by the coefficients. However, MSC, like the solutions in [1,[9][10][11], is a supervised approach that asks for sufficient labeled images for sparse representation and dimensionality reduction. Nevertheless, it is very time consuming and expensive to collect sufficient labeled images.…”

Section: Related Workmentioning

confidence: 99%

“…Remote sensing image annotation [1] aims to assign one or several predefined labels (semantic concepts) to a remote sensing image. It is the basis of remote sensing image indexing for organizing and locating images of interest from a large database.…”

Section: Introductionmentioning

confidence: 99%

“…Given that, it is necessary to take into account the multi labels' characteristics of remote sensing images and annotate a set of relevant labels to these images, instead of a single label. Recently, multi-label classification [7,8] that studies the problem where an instance annotated with a set of labels has been applied to annotate multi-label remote sensing images [1,[9][10][11] and has shown appealing performance in remote sensing applications. The multi-label classification framework has been applied to numerous real-world applications [12].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

et al. 2017

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Abstract:Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.

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“…In recent years, mainstream studies on recognizing urban land-use patterns have been based on high spatial resolution (HSR) image classification (Bratasanu et al, 2011;Chen et al, 2013;Wen et al, 2016;Zhong et al, 2015). HSR images contain a substantial amount of natural-physical geospatial information, which is widely used in the object-oriented classification (OOC) method to extract urban land-use information (Bratasanu et al, 2011;Durand et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

Yao

Liang

Xia

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

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Semantic Annotation of High-Resolution Remote Sensing Images via Gaussian Process Multi-Instance Multilabel Learning

Cited by 23 publications

References 10 publications

Remote Sensing Image Classification With Large-Scale Gaussian Processes

Remote Sensing Image Classification With Large-Scale Gaussian Processes

Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

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