人工智能
格拉米安矩阵
模式识别(心理学)
计算机科学
系列(地层学)
特征提取
编码(内存)
领域(数学)
上下文图像分类
特征(语言学)
时间序列
图像(数学)
计算机视觉
数学
机器学习
古生物学
生物
语言学
特征向量
物理
哲学
量子力学
纯数学
作者
Danielle Dias,Ulisses Dias,Nathalia Menini,Rubens Augusto Camargo Lamparelli,Guerric Le Maire,Ricardo da S. Torres
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2020-08-01
卷期号:17 (8): 1450-1454
被引量:20
标识
DOI:10.1109/lgrs.2019.2946951
摘要
Pixelwise image classification based on time series profiles has been very effective in several applications. In this letter, we investigate recently proposed image-based time series encoding approaches [e.g., Gramian angular summation field/Gramian angular difference field (GASF/GADF) and Markov transition field (MTF)] to support the identification of eucalyptus regions in remote sensing images. We perform a comparative study concerning the combination of image-based representations suitable for encoding the most important time series patterns with the ability of state-of-the-art deep-learning-based approaches for characterizing image visual properties. The comparative study demonstrates that the evaluated image representations, combined with different deep learning feature extractors lead to highly effective classification results, which are superior to those of recently proposed methods for time-series-based eucalyptus plantation detection.
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