A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

By Ce Zhang, Xin Pan, Huapeng Li, A. Gardiner, Isabel Sargent, Jonathon S. Hare, Peter M. Atkinson. Isprs Journal of Photogrammetry and Remote Sensing, 2017

391 citations recorded by the source corpus.

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.

Subfields: Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/137e8317a8681aa615a22f3638ba97761d76cb97

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Citations in the corpus

Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, neutral): “, in optimization solutions) [29].”
Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery (2018, methodology): “An optimal patch size was determined using a trial-and-error procedure, by taking into account a spatial resolution of 5 m for the input image and the contextual relationship of the objects [41].”
Building Instance Classification Using Street View Images (2018, background): “Recently, many approaches based on deep learning techniques have emerged (Cheng et al., 2017; Ma et al., 2016; Zhang et al., 2018).”
Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images (2020, background): “Meanwhile, different classifiers have been designed from diverse perspectives, from traditional methods such as logistic regression [13], distance measures [14] and clustering [15], to more advanced machine learning methods such as the support vector machine (SVM) [16], random forest (RF) [17] and a”