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An Improved Pretraining Strategy-Based Scene Classification With Deep Learning

机译：基于预磨练的基于策略的场景分类，深入学习

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摘要

High-resolution remote sensing (HRRS) image scene classification takes an important role in many applications and has attracted much attention. Recently, notable efforts have been made to present massive methods for HRRS scene classification, wherein deep-learning-based methods demonstrate remarkable performance compared with state-of-the-art methods. However, HRRS images contain complex contextual relationships and large differences of object scale, which are significantly different from natural images. The existing deep-learning-based scene classification methods are originally designed for natural image processing and have not been optimized to adapt to the characteristics of HRRS images, which significantly affects the efficiency of the feature extraction and recognition accuracy. In addition, when designing a model for remote sensing tasks, the pretraining of the model is time-consuming. The enormous amount of pretraining time and computation resources necessarily increase the difficulty of producing an excellent model. In this letter, focusing on the problems above, we proposed a new convolutional neural network (CNN)-based scene classification method. The CNN-based scene classification method is constructed by spatial-scale-aware blocks and is efficient in extracting the abundant spatial features, but can also adaptively adjust feature responses to maximize the function of informative features in the classification results. In addition, an HRRS imagery-based learning strategy is utilized to obtain an initial model for fine-tuning the model parameters, which drastically reduces the pretraining time. The proposed method has been demonstrated using two HRRS data sets, and experimental results have proven the superiority of the proposed method.
机译：高分辨率遥感（HRRS）图像场景分类在许多应用中取得了重要作用，并引起了很多关注。最近，已经对HRRS场景分类的大规模方法进行了显着的努力，其中基于深度学习的方法与最先进的方法相比，表现出显着的性能。然而，HRRS图像包含复杂的上下文关系和对象量表的大差异，与自然图像有显着不同。最初设计了现有的基于深建的场景分类方法，用于自然图像处理，并且未得到优化以适应HRRS图像的特性，这显着影响特征提取和识别精度的效率。此外，在设计用于遥感任务的模型时，模型的预先磨平是耗时的。巨大的预先预测时间和计算资源必须增加产生优秀模型的难度。在这封信中，专注于上述问题，我们提出了一种新的卷积神经网络（CNN）基础的场景分类方法。基于CNN的场景分类方法由空间尺度感知块构建，并且在提取丰富的空间特征方面是有效的，而是可以自适应地调整特征响应，以最大化分类结果中的信息特征的功能。此外，利用基于HRRS图像的学习策略来获得微调模型参数的初始模型，这大大减少了预先降低的时间。已经使用两个HRRS数据集进行了拟议的方法，实验结果证明了所提出的方法的优势。

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