【计算机科学】【2016.05】利用深度学习技术进行土地利用和土地覆盖分类_土地利用分类 深度学习

本文为美国亚利桑那州立大学（作者：Nagesh Kumar Uba）的硕士论文，共53页。

以正射影像拼接为代表的亚米级航空影像的大型数据集在今天已广泛应用，这些数据集可能包含大量未开发的信息。此类图像有可能定位几种类型的特征，例如，图像中的森林、停车场、机场、住宅区或高速公路。然而，这些特征的出现会受到很多因素的影响，包括图像被捕获的时间、传感器设置、为纠正图像所做的处理以及图像所捕获区域的地理和文化背景。

本论文探讨利用深度卷积神经网路将土地利用从甚高空间分辨率（VHR）、正射校正、可见波段多光谱影像中进行分类。最近的技术和商业应用推动了在可见的红、绿、蓝（RGB）光谱波段收集到大量的VHR图像，这项工作探索了深度学习算法利用这些图像进行自动土地利用/土地覆盖（LULC）分类的潜力。自动可见波段VHR LULC分类的好处可能包括自动变化检测或映射等应用。最近的研究显示了土地利用分类的深度学习方法的潜力；然而，本论文通过应用其他数据集增强方法来改进当前最先进的方法，这些方法非常适合地理空间数据的处理。此外，通过对未知数据集上的分类器进行广泛评估，检验了分类器的可归纳性，并给出了分类器的准确度水平，以证明分类结果实际上超越了训练中使用的小型基准数据集。

深度网络具有许多参数，因此它们通常使用非常大的标记数据集来构建。适合于LULC的大型数据集并不容易获得，但是诸如精化学习之类的技术允许为一个任务训练的网络重新训练，以执行另一个识别任务。这篇论文的贡献包括证明在一个任务中为图像识别训练的深度网络（ImageNet）可以有效地传输到遥感应用程序，并且在不需要大量训练数据集的情况下，其性能与人工设计的分类器相当或更好。这一点在UC Merced数据集中得到了证明，其中96%的平均准确度是通过CNN（卷积神经网络）和5重交叉验证实现的。这些结果将在与训练集分辨率相同的无关VHR图像上进行进一步测试。

Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification. The benefits of automatic visible band VHR LULC classifications may include applications such as automatic change detection or mapping. Recent work has shown the potential of Deep Learning approaches for land use classification; however, this thesis improves on the state-of-the-art by applying additional dataset augmenting approaches that are well suited for geospatial data. Furthermore, the generalizability of the classifiers is tested by extensively evaluating the classifiers on unseen datasets and we present the accuracy levels of the classifier in order to show that the results actually generalize beyond the small benchmarks used in training. Deep networks have many parameters, and therefore they are often built with very large sets of labeled data. Suitably large datasets for LULC are not easy to come by, but techniques such as refinement learning allow networks trained for one task to be retrained to perform another recognition task. Contributions of this thesis include demonstrating that deep networks trained for image recognition in one task (ImageNet) can be efficiently transferred to remote sensing applications and perform as well or better than manually crafted classifiers without requiring massive training data sets. This is demonstrated on the UC Merced dataset, where 96% mean accuracy is achieved using a CNN (Convolutional Neural Network) and 5-fold cross validation. These results are further tested on unrelated VHR images at the same resolution as the training set.

1 引言

2 背景文献

3 设计方法

4 数据分析与结果

5 讨论

附录 林木提取

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