Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

By Bo Huang, Bei Zhao, Yimeng Song. Remote Sensing of Environment, 2018

545 citations recorded by the source corpus.

Abstract Urban land-use mapping is a significant yet challenging task in the field of remote sensing. Although numerous classification methods have been developed for obtaining land-use information in urban areas, the accuracy and efficiency of these methods are insufficient to meet the requirements of real-world applications such as urban planning and land management. In recent years, deep learning techniques, especially deep convolutional neural networks (DCNN), have achieved an astonishing level of performance in image classification. However, the traditional DCNN methods do not focus on multispectral remote sensing images with more than three channels, and they are limited by their training samples. In addition, these methods uniformly decompose large images into small processing units, which chop up the land-use patterns and produce land-use maps with obvious “blocks”. In this study, a semi-transfer deep convolutional neural network (STDCNN) approach is proposed to overcome these weaknesses. The proposed STDCNN has three parts: one part involves a transferred DCNN with deep architecture; another part is designed to analyze multispectral images; and the final part fuses the first two parts into a classification layer. Moreover, a skeleton-based decomposing method using street block data is devised to maintain the integrity of the land-use patterns. In two case studies, the proposed method is used to generate urban land-use maps from a WorldView-3 image of a 143 km2 area of Hong Kong and a WorldView-2 image of a 25 km2 area of Shenzhen. The results show that the proposed STDCNN obtains an overall accuracy (OA) of 91.25% and a Kappa coefficient (Kappa) of 0.903 for Hong Kong land-use classification, and an OA of 80% and a Kappa of 0.780 for Shenzhen land-use classification. In addition, due to the proposed skeleton-based decomposition method, the proposed method can produce better land-use maps for real-world urban applications.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/cbfc673d0b82036142a08849fd86275bbcc4eba2

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In Atmospheric Science

In Global and Planetary Change

Liangpei Zhang

In Media Technology

Citations in the corpus

Land-cover classification with high-resolution remote sensing images using transferable deep models (2018, background): “To reduce the required number of training samples, semitransfer deep convolutional neural networks (STDCNN) is proposed for land-use mapping [90].”
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”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “The classification of wetlands [12,13], urban land-use [14,15], agriculture [16], forest [17], and other vegetation mappings are some examples of the application-focused LULC classification approaches that are available in the literature.”
Change detection techniques for remote sensing applications: a survey (2019, background): “(Huang et al. 2018a, b, c, d) presents a tensor and deep learning based change detection.”
Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “DL has been recently introduced to land cover mapping and obtained optimal results due to its superiorities in multiscale and multilevel feature extraction (Huang et al., 2018; Scott et al., 2017b; Zhang et al., 2019).”