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.

中文翻译：

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具有潜在语义依赖性的多标签遥感图像分类

亚马逊雨林中的森林砍伐导致生物多样性减少，生境丧失，气候变化和其他破坏性影响。因此，获得有关人类活动的位置信息对于致力于保护亚马逊雨林的科学家和政府至关重要。我们提出了一种新颖的遥感影像分类框架，该框架为我们提供了更有效地管理森林砍伐及其后果所需的关键数据。我们引入了关注模块，以按通道分离从CNN（卷积神经网络）中提取的特征，然后将分离的特征进一步发送到LSTM（长短期记忆）网络以顺序预测标签。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。 $F_2$ 得分与其他方法相比，表明我们的模型在利用标签依存关系提高多标签图像分类性能方面是有效的。