Surface object recognition with CNN and SVM in Landsat 8 images
Surface object recognition with CNN and SVM in Landsat 8 images
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DOI：
10.1109/mva.2015.7153200
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Tomohiro Ishii;R. Nakamura;H. Nakada;Yoshihiko Mochizuki;H. Ishikawa
中科院分区：
文献类型：
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作者：
Tomohiro Ishii;R. Nakamura;H. Nakada;Yoshihiko Mochizuki;H. Ishikawa
有一系列被称为陆地卫星的地球观测卫星，它们每天发送非常大量的图像数据，以至于很难人工分析。因此，需要有效地应用机器学习技术来自动分析这些数据。在表面物体识别中，测量特定物体在表面上的分布，是这类数据的重要应用之一。本文提出并比较了两种表面目标识别方法，一种是卷积神经网络（CNN），另一种是支持向量机（SVM）。在我们的实验中，CNN表现出比SVM更高的性能。此外，我们观察到负样本的数量对性能有影响，在实际使用中有必要选择负样本的数量。
There is a series of earth observation satellites called Landsat, which send a very large amount of image data every day such that it is hard to analyze manually. Thus an effective application of machine learning techniques to automatically analyze such data is called for. In surface object recognition, which is one of the important applications of such data, the distribution of a specific object on the surface is surveyed. In this paper, we propose and compare two methods for surface object recognition, one using the convolutional neural network (CNN) and the other support vector machine (SVM). In our experiments, CNN showed higher performance than SVM. In addition, we observed that the number of negative samples have a influence on the performance, and it is necessary to select the number of them for practical use.