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Scene Classification in High Resolution Remotely Sensed Images Based on PCANet

机译：基于PCANet的高分辨率遥感影像场景分类

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摘要

Rich information provided by high resolution remotely sensed images allow us to classify scenes by understanding their spatial and structural patterns. The key of scene classification task with remotely sensed images lies in feature learning efficiency and invariant image representations. While deep neutral network-based approaches achieved good classification accuracy for remotely sensed images, they often have to train millions of parameters and involve heavily iterative computation. In this paper, we propose a new framework for scene classification based on a simple PCANet which is introduced into high remotely sensed image classification for the first time. First, we verify the eligibility of PCANet on classifying large scale scenes from high resolution remotely sensed images. Then we explore the impact of PCANet parameters including filter size, number of filters, and block overlap ratio on classification accuracy. Lastly, we do comprehensive experiments with the public UC-Merced dataset to exemplify the effectiveness of the approach. Experimental results show that the proposed framework achieved on par with the state-of-the-art deep neutral network-based classification accuracy without training a huge amount of parameters. We demonstrate that the proposed classification framework can be highly effective in developing a classification system that can be used to automatically scan large-scale high resolution satellite imagery for classifying scenes.
机译：高分辨率遥感影像提供的丰富信息使我们能够通过了解场景的空间和结构模式来对其分类。具有遥感图像的场景分类任务的关键在于特征学习效率和不变的图像表示。尽管基于深度中性网络的方法对遥感图像具有良好的分类精度，但它们通常必须训练数百万个参数并涉及大量的迭代计算。在本文中，我们提出了一个基于简单PCANet的场景分类新框架，该框架首次被引入到高遥感图像分类中。首先，我们验证了PCANet在从高分辨率遥感影像中对大型场景进行分类的资格。然后，我们探讨了PCANet参数（包括滤波器大小，滤波器数量和块重叠率）对分类准确性的影响。最后，我们使用公开的UC-Merced数据集进行了综合实验，以证明该方法的有效性。实验结果表明，所提出的框架在不训练大量参数的情况下，可与基于最新的深度中立网络的分类精度相媲美。我们证明了所提出的分类框架在开发分类系统方面可以非常有效，该分类系统可用于自动扫描大规模高分辨率卫星图像以对场景进行分类。

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