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首页> 外文期刊>Journal of Applied Remote Sensing >Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery
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Scene classification based on the bag-of-visual-words and Doc2Vec models for high-spatial resolution remote-sensing imagery

机译：基于Visual-Lock和Doc2Vec模型的场景分类，用于高空间分辨率遥感图像

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摘要

A probabilistic topic model (PTM) combined with the bag-of-visual-words model is a common method to bridge the so-called "semantic gap" problem in remote-sensing image classification research. Owing to the inherent shortcomings of PTMs, such as time consumption and failures to consider a spatial arrangement of various objects, we introduce a natural language processing document-to-vector (Doc2Vec) model, to capture the high-level semantic information of the images, instead of a PTM. The model characterizes words and documents as dense, low-dimensional vectors and implements a simplified, shallow neural network to train a language model and word vectors. It is expected to mine semantic information of remote-sensing images from a new perspective. We also improve the low-level feature quality by using feature-specific sampling methods. Two high-spatial resolution remote-sensing image datasets, UC Merced and RSSCN7, are employed to conduct a scene classification experiment to discuss the performance of the Doc2Vec model. The experimental results show that the Doc2Vec model is highly efficient in mining semantic information of the images, compared with the state-of-the-art methods. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)
机译：概率主题模型（PTM）与Visual-locks模型相结合是一种常见的方法，可以在遥感图像分类研究中弥合所谓的“语义差距”问题。由于PTM的固有缺点，例如时间消耗和失败来考虑各种对象的空间安排，我们介绍了一种自然语言处理文档到矢量（DOC2VEC）模型，以捕获图像的高电平语义信息，而不是ptm。该模型将单词和文档表征为密集，低维向量，实现简化的浅神经网络，以培训语言模型和字向量。预计将从新的视角挖掘遥感图像的语义信息。我们还通过使用特定于特征的采样方法来提高低级特征质量。两个高空间分辨率遥感图像数据集，UC Merced和RSSCN7，用于进行场景分类实验，讨论Doc2VEC模型的性能。实验结果表明，与最先进的方法相比，DOC2VEC模型在图像的挖掘语义信息中具有高效。 （c）2019年光学仪表工程师协会（SPIE）

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