Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.

中文翻译：

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基于Classifier-Fusion的自适应深度共现特征学习用于遥感场景分类

遥感场景分类在土地覆盖土地利用方面有许多应用。然而，将场景图像分类为正确的类别是一项具有挑战性的任务。这一挑战归因于遥感图像的不同语义。遥感图像的这种性质使得有效特征提取和学习的任务变得复杂。有效的图像特征表示对于使用机器学习算法进行准确的场景图像分类的图像分析和解释至关重要。最近的文献表明，卷积神经网络在遥感场景分类的特征提取方面是强大的。此外，最近的文献表明，分类器融合比单个分类器获得了更好的结果。本文提出了自适应深度协同特征学习（ADCFL）。ADCFL 方法利用卷积神经网络以与滤波器共现的方式从图像中提取空间特征信息，然后将这些信息通过集成分类器的多数票输入多粒森林进行特征学习和分类。在公共数据集 Resisc45 和 Ucmerced 上对 ADCFL 的有效性进行了评估。ADCFL 获得的分类精度结果表明所提出的方法取得了改进的结果。在公共数据集 Resisc45 和 Ucmerced 上对 ADCFL 的有效性进行了评估。ADCFL 获得的分类精度结果表明所提出的方法取得了改进的结果。在公共数据集 Resisc45 和 Ucmerced 上对 ADCFL 的有效性进行了评估。ADCFL 获得的分类精度结果表明所提出的方法取得了改进的结果。