Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies
Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies
复制标题
具有潜在语义依赖性的多标签遥感图像分类
DOI：
10.3390/rs12071110
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发表时间：
2020-04-01
期刊：
影响因子：
5
通讯作者：
Song, Houbing
中科院分区：
文献类型：
--
作者：
Ji, Junchao;Jing, Weipeng;Song, Houbing
亚马逊雨林的森林砍伐导致生物多样性减少、栖息地丧失、气候变化和其他破坏性影响。因此，获取人类活动的位置信息对于保护亚马逊雨林的科学家和政府来说至关重要。我们提出了一个新的遥感图像分类框架，为我们提供更有效地管理森林砍伐及其后果所需的关键数据。我们引入了注意模块，将卷积神经网络中提取的特征按通道进行分离，然后将分离出来的特征依次发送到LSTM（长短期记忆）网络进行标签预测。此外，我们通过计算数据集中所有标签的共现矩阵并为每个标签分配不同的权重来提出损失函数。在亚马逊雨林卫星图像数据集上的实验结果表明，与其他方法相比，我们的模型获得了更好的f2分数，这表明我们的模型可以有效地利用标签依赖关系来提高多标签图像分类的性能。
Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification.