An object-based convolutional neural network (OCNN) for urban land use classification

By Ce Zhang, Isabel Sargent, Xin Pan, Huapeng Li, A. Gardiner, Jonathon S. Hare, Peter M. Atkinson. Remote Sensing of Environment, 2018

423 citations recorded by the source corpus.

Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

Subfields: Atmospheric Science, Media Technology, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/ab45b7a706129f2032167ff40d9e3cf88fb804a1

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Citations in the corpus

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (2020, methodology): “Semantic Segmentation in Remote Sensing Community There are a lot of applications using semantic segmentation technique in the remote sensing community, such as land use and land cover (LULC) classification [46, 23, 47], building extraction [45, 24, 43, 15], road extraction [28, 9, 2, 34, 3], vehicl”
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection (2019, methodology): “[59] used different sizes of input windows and developed a similar approach for the detection of complex shape objects in urban areas.”
Joint Deep Learning for land cover and land use classification (2019, methodology): “…multilayer 131 perceptron (MLP) method to solve LC classification with spatial feature representation and 132 pixel-level differentiation; Zhang et al. (2018b) proposed a regional fusion decision strategy 133 based on rough set theory to model the uncertainties in LC classification of…”