A HYBRID DILATION APPROACH FOR REMOTE SENSING SCENE IMAGE CLASSIFICATION

IF 0.2 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Anas Tukur Balarabe, I. Jordanov
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Abstract

While fine-tuning a transfer learning model alleviates the need for a vast amount of training data, it still comes with a few challenges. One of them is the range of image dimensions that the input layer of a model accepts. This issue is of interest, especially in tasks that require the use of a transfer learning model. In scene classification, for instance, images could come in varying sizes that could be too large/small to be fed into the first layer of the architecture. While resizing could be used to trim images to a required shape, that is usually not possible for images with tiny dimensions, for example, in the case of the EuroSAT dataset. This paper proposes an Xception model-based framework that accepts images of arbitrary size and then resizes or interpolates them before extracting and enhancing the discriminative features using an adaptive dilation module. After applying the approach for scene classification problems and carrying out a number of experiments and simulations, we achieved 98.55% accuracy on the EuroSAT dataset, 99.22% on UCM , 96.15% on AID and 96.04% on the SIRI-WHU dataset, respectively. We also monitored the micro-average and macro-average ROC curve scores for all the datasets to further evaluate the proposed model’s effectiveness.
一种用于遥感场景图像分类的混合扩展方法
虽然微调迁移学习模型减轻了对大量训练数据的需求,但它仍然存在一些挑战。其中之一是模型的输入层接受的图像尺寸范围。这个问题很有趣,特别是在需要使用迁移学习模型的任务中。例如,在场景分类中,图像可能有不同的大小,可能太大或太小而无法输入到架构的第一层。虽然调整大小可以用来将图像修剪成所需的形状,但对于尺寸很小的图像来说,这通常是不可能的,例如,在EuroSAT数据集的情况下。本文提出了一种基于异常模型的框架,该框架接受任意大小的图像,然后调整大小或插值,然后使用自适应扩展模块提取和增强判别特征。将该方法应用于场景分类问题,并进行了大量的实验和模拟,结果表明,该方法在EuroSAT数据集上的准确率为98.55%,在UCM数据集上的准确率为99.22%,在AID数据集上的准确率为96.15%,在SIRI-WHU数据集上的准确率为96.04%。我们还监测了所有数据集的微观平均值和宏观平均值ROC曲线得分,以进一步评估所提出模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IADIS-International Journal on Computer Science and Information Systems
IADIS-International Journal on Computer Science and Information Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
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