细粒度显著区域引导的遥感图像场景分类

Scene Classification of Remote Sensing Images Guided by Fine-Grained Salient Region

近年来,遥感图像场景在监测环境、勘探地球资源及预测自然灾害等方面有着越来越广泛的应用,大量的数据需求推动了遥感图像场景分类的快速发展。尽管基于深度学习的方法已经在场景分类方面取得了比较好的性能,但如何对背景复杂、尺度变化剧烈的遥感场景进行有效识别仍然是分类任务中的一个巨大挑战。为了解决这一问题,提出一种细粒度方法来检测显著区域,并使用全局分支和局部分支将整体和局部联合起来,分别从整幅图像和关键区域提取全局特征和局部关键信息。为了验证所提方法的有效性,基于ResNet18模型在三个公共遥感图像场景分类数据集上对不同方法进行对比实验,实验结果表明所提方法的准确率优于大多数先进方法。

Recently,remote sensing image scenes have been increasingly widely used in monitoring the environment,exploring earth resources,and predicting natural disasters.Numerous data requirements aid in the rapid development of remote sensing image scene classification.Although the deep learningbased method has achieved decent performance in scene classification,how to effectively classify remote sensing scenes with complex backgrounds and drastic scale changes remains a great challenge in the classification task.To address this issue,this paper proposes a finegrained approach to detect the salient region,uses the global and local branches to combine the global and local parts,and extracts the global features and local key information from the whole image and key region,respectively.To verify the effectiveness of the proposed method,comparative experiments are conducted using ResNet18 on three public remote sensing image scene classification datasets,and the experimental results show that the accuracy of the proposed method is better than that of most advanced methods.