Classification, recognition, and quality assessment of aerial images strongly depends on detecting and identifying their discriminative visual features. In practice, aerial images provide clues for various applications, including disaster prediction, automatic navigation, and military target detection. However, the detection of discriminative cues in aerial images is quite problematic since the aerial image quality is susceptible to luminance and noise, while aerial images have significantly different topological structures. We propose a novel method to explore quality-related and topological cues from aerial images for visual classification to mitigate these problems. We first decompose aerial images into several components, each being processed via the morphological filtering. Subsequently, we leverage the quality model to generate discriminative regions and topologies. Each aerial image is represented using a feature vector extracted from these regions. Afterward, we train a CNN-based visual classification model to predict aerial image categories. Experimental results have shown that our method can effectively predict aerial image categories, and the proposed algorithm is more robust than other state-of-the-art ones.

航拍图像的分类，识别和质量评估在很大程度上取决于检测和识别其区分性视觉特征。实际上，航空影像为各种应用提供了线索，包括灾难预测，自动导航和军事目标检测。但是，由于航拍图像质量容易受到亮度和噪声的影响，而航拍图像具有明显不同的拓扑结构，因此航拍图像中判别线索的检测存在很大问题。我们提出了一种新颖的方法，可以从航空影像中探索与质量相关的拓扑线索，以进行视觉分类以缓解这些问题。我们首先将航空图像分解为几个组成部分，每个组成部分均通过形态学滤波处理。随后，我们利用质量模型来生成可区分的区域和拓扑。使用从这些区域提取的特征向量表示每个航空图像。之后，我们训练基于CNN的视觉分类模型来预测航空影像类别。实验结果表明，我们的方法可以有效地预测航空图像的类别，并且所提出的算法比其他最新技术更健壮。