Applications of unmanned aerial vehicles (UAVs) based remote sensing is increasing rapidly due to their advanced accessibility, capability for fast and easy deployment, capability for miniaturization of sensors and efficient collection of remotely-sensed data from relatively low altitudes. Recently, UAV data sets have been found to be quite useful for forest feature identification due to their relatively high spatial resolution. Several machine learning algorithms have been broadly used for remotely-sensed image classification. In remote sensing image classification, deep learning based methods can be considered quite effective techniques as they have achieved promising results. In this study, we have used deep learning based supervised image classification algorithm and images collected using UAV for classification of forest areas. The deep learning algorithm stacked Auto-encoder has been found to have tremendous potential regarding image classification and the assessment of forest coverage area. Our experimental results show that deep learning method provides better accuracy compared to other machine learning algorithms. Cross-validation showed that the overall accuracy of the deep learning method is about 93%. This study highlights the essential role that UAV observations and deep learning could play in the planning and management of forest areas which are often under the threat of deforestation and forest encroachment.

中文翻译：

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基于深度学习的监督图像分类使用无人机图像进行森林区域分类

基于无人机 (UAV) 的遥感应用因其先进的可访问性、快速简便的部署能力、传感器的小型化能力以及从相对低的高度有效收集遥感数据而迅速增加。最近，已经发现无人机数据集由于其相对较高的空间分辨率而对森林特征识别非常有用。几种机器学习算法已广泛用于遥感图像分类。在遥感图像分类中，基于深度学习的方法可以被认为是非常有效的技术，因为它们取得了可喜的成果。在这项研究中，我们使用基于深度学习的监督图像分类算法和使用无人机收集的图像进行森林区域分类。已发现堆叠自动编码器的深度学习算法在图像分类和森林覆盖面积评估方面具有巨大潜力。我们的实验结果表明，与其他机器学习算法相比，深度学习方法提供了更好的准确性。交叉验证表明，深度学习方法的整体准确率约为 93%。这项研究强调了无人机观测和深度学习在经常受到森林砍伐和森林侵占威胁的林区的规划和管理中可以发挥的重要作用。我们的实验结果表明，与其他机器学习算法相比，深度学习方法提供了更好的准确性。交叉验证表明，深度学习方法的整体准确率约为 93%。这项研究强调了无人机观测和深度学习在经常受到森林砍伐和森林侵占威胁的林区的规划和管理中可以发挥的重要作用。我们的实验结果表明，与其他机器学习算法相比，深度学习方法提供了更好的准确性。交叉验证表明，深度学习方法的整体准确率约为 93%。这项研究强调了无人机观测和深度学习在经常受到森林砍伐和森林侵占威胁的林区的规划和管理中可以发挥的重要作用。