Object-Oriented Mangrove Species Classification Using Hyperspectral Data and 3-D Siamese Residual Network

IF 4 3区 地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Geoscience and Remote Sensing Letters Pub Date : 2020-12-01 DOI:10.1109/LGRS.2019.2962723
Zhi He, Q. Shi, Kai Liu, Jingjing Cao, Wen Zhan, B. Cao
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引用次数: 13

Abstract

Mangrove species classification is of particular importance for coastal conservation and restoration. However, it is challenging to distinguish species-level differences with limited training data. In this letter, we propose an object-oriented classification method for mangrove forests by using the hyperspectral image (HSI) and the 3-D Siamese residual network. First, superpixel segmentation is utilized to obtain objects with various shapes and scales. Second, 3-D patches of each object are extracted from the original HSI, and those patches containing training samples are adopted to pairwise train the network. The 3-D spatial pyramid pooling (3-D-SPP) is added in the network to extract features in multiple scales. Finally, the abstract features of test samples are learned by the trained network, and the labels are determined by the nearest neighbor classifier within the metric space. Experiments on real mangrove hyperspectral data demonstrate the effectiveness of the proposed method in species classification of mangroves.
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基于高光谱数据和三维暹罗残差网络的面向对象红树林物种分类
红树林物种分类对海岸保护和恢复具有特别重要的意义。然而,用有限的训练数据来区分物种水平的差异是具有挑战性的。在这封信中,我们提出了一种利用高光谱图像(HSI)和三维暹罗残差网络对红树林进行面向对象分类的方法。首先,利用超像素分割来获得具有各种形状和尺度的对象。其次,从原始HSI中提取每个对象的三维补丁,并采用那些包含训练样本的补丁来成对训练网络。在网络中添加了三维空间金字塔池(3-D-SPP)来提取多尺度的特征。最后,通过训练的网络学习测试样本的抽象特征,并通过度量空间内的最近邻分类器确定标签。在真实红树林高光谱数据上的实验证明了该方法在红树林物种分类中的有效性。
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来源期刊
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理
CiteScore
7.60
自引率
12.50%
发文量
1113
审稿时长
3.4 months
期刊介绍: IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.
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