The classification of natural terrain ahead of an autonomous vehicle can help it make decisions regarding the traversability of various paths, but remains an open research problem. Despite the explosion in popularity of deep learning networks,there is still little work available on informed neural network design procedures for specific tasks such as terrain image classification, save through performance measures. A related problem is understanding features of a dataset that lead to difficulties in separating classes of images from one another. This research proposes an algorithm and accompanying analytical procedure to characterize such image classification difficulties; identifying what makes some images easily distinguishable as their class and what makes others readily confused with other classes. This is achieved by learning reduced-dimensionality representations of the input data, constructing a convex hull of class members in the reduced dimensionality representation, then examining between-class overlap within each space, incrementally increasing the dimensionality until overlap is eliminated. Summarizing the between-class overlap statistics reveals trends and anomalies that can be linked back to visual features, characteristic of the original data. Case studies are presented of insights identified through selected example analyses: relative intensities of terrain classes from images taken by Mars rovers, and the impact of color gradients in separating sand from bedrock in color images of terrain. Such insights are discussed as steps toward a more directed approach to designing neural networks for image classification.

自动驾驶汽车前方自然地形的分类可以帮助它做出关于各种路径的可穿越性的决定，但仍然是一个开放的研究问题。尽管深度学习网络的流行度呈爆炸式增长，但除了通过性能测量外，对于特定任务（如地形图像分类）的知情神经网络设计程序仍然很少有工作可用。一个相关的问题是理解数据集的特征，这导致难以将图像类别彼此分离。本研究提出了一种算法和伴随的分析程序来表征此类图像分类困难；确定是什么使某些图像易于区分为它们的类，以及是什么使其他图像易于与其他类混淆 这是通过学习输入数据的降维表示，在降维表示中构建类成员的凸包，然后检查每个空间内的类间重叠，逐步增加维度直到消除重叠来实现的。总结类间重叠统计揭示了趋势和异常，这些趋势和异常可以链接回视觉特征，原始数据的特征。案例研究介绍了通过选定示例分析确定的见解：来自火星探测器拍摄的图像的地形类别的相对强度，以及颜色梯度在地形彩色图像中将沙子与基岩分离的影响。这些见解被讨论为朝着更直接的方法设计用于图像分类的神经网络的步骤。