Journal ArticleDOI
Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation
M. Lienou,H. Maitre,Mihai Datcu +2 more
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TLDR
This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches, using the maximum-likelihood method.Abstract:
In this letter, we are interested in the annotation of large satellite images, using semantic concepts defined by the user. This annotation task combines a step of supervised classification of patches of the large image and the integration of the spatial information between these patches. Given a training set of images for each concept, learning is based on the latent Dirichlet allocation (LDA) model. This hierarchical model represents each item of a collection as a random mixture of latent topics, where each topic is characterized by a distribution over words. The LDA-based image representation is obtained using simple features extracted from image words. We then exploit the capability of the LDA model to assign probabilities to unseen images, in order to classify the patches of the large image into the semantic concepts, using the maximum-likelihood method. We conduct experiments on panchromatic QuickBird images with 60-cm resolution. Taking into account the spatial information between the patches shows to improve the annotation performance.read more
Citations
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Journal ArticleDOI
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Liangpei Zhang,Lefei Zhang,Bo Du +2 more
TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
Journal ArticleDOI
Remote Sensing Image Scene Classification: Benchmark and State of the Art
TL;DR: A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).
Journal ArticleDOI
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
Gui-Song Xia,Jingwen Hu,Fan Hu,Baoguang Shi,Xiang Bai,Yanfei Zhong,Liangpei Zhang,Xiaoqiang Lu +7 more
TL;DR: The Aerial Image Data Set (AID) as mentioned in this paper is a large-scale data set for aerial scene classification, which contains more than 10,000 aerial images from remote sensing images.
Journal ArticleDOI
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
TL;DR: The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.
Journal ArticleDOI
Remote Sensing Image Scene Classification: Benchmark and State of the Art
TL;DR: In this paper, the authors proposed a large-scale data set, termed "NWPU-RESISC45", which is a publicly available benchmark for remote sensing image scene classification (RESISC), created by Northwestern Polytechnical University.
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Proceedings ArticleDOI
A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei,Pietro Perona +1 more
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.
Journal ArticleDOI
Unsupervised Learning by Probabilistic Latent Semantic Analysis
TL;DR: This paper proposes to make use of a temperature controlled version of the Expectation Maximization algorithm for model fitting, which has shown excellent performance in practice, and results in a more principled approach with a solid foundation in statistical inference.
Book ChapterDOI
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
TL;DR: This work shows how to cluster words that individually are difficult to predict into clusters that can be predicted well, and cannot predict the distinction between train and locomotive using the current set of features, but can predict the underlying concept.