The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

具有深层结构的基于上下文的卷积神经网络（CNN）和具有浅层结构的基于像素的多层感知器（MLP）是公认的神经网络算法，代表了最新的深度学习方法和经典的非学习算法。参数化机器学习方法。两种算法的行为截然不同，它们通过基于规则的决策融合方法以一种简洁有效的方式集成在一起，用于对非常精细的空间分辨率（VFSR）遥感影像进行分类。主要基于CNN的分类置信度设计的决策融合规则反映了各个分类器的一般互补模式。结果，拟议的集成分类器MLP-CNN收集了基于深度空间特征表示的从CNN和基于光谱识别的MLP所获得的互补结果。同时，补偿了由于采用卷积滤波器而导致的CNN的局限性，例如对象边界划分的不确定性和有用的精细空间分辨率细节的丢失。使用航空摄影和其他卫星传感器数据集，在城市和农村地区测试了集成MLP-CNN分类器的有效性。MLP-CNN分类器取得了可喜的性能，在分类准确度方面始终优于基于像素的MLP，基于光谱和纹理的MLP和基于上下文的CNN。