Land-use Mapping for High Spatial Resolution Remote Sensing Image via Deep Learning: A Review

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

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An Augmentation Attention Mechanism for High-Spatial-Resolution Remote Sensing Image Scene Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.3006241 ◽

2020 ◽

Vol 13 ◽

pp. 3862-3878

Author(s):

Fengpeng Li ◽

Ruyi Feng ◽

Wei Han ◽

Lizhe Wang

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Image ◽

Attention Mechanism ◽

Scene Classification

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

Remote Sensing of Environment ◽

10.1016/j.rse.2018.04.050 ◽

2018 ◽

Vol 214 ◽

pp. 73-86 ◽

Cited By ~ 128

Author(s):

Bo Huang ◽

Bei Zhao ◽

Yimeng Song

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Land Use ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Urban Land Use ◽

Deep Convolutional Neural Network ◽

Remote Sensing Imagery ◽

Multispectral Remote Sensing

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Rural settlements extraction based on deep learning from high spatial resolution remote sensing imagery

MIPPR 2019: Pattern Recognition and Computer Vision ◽

10.1117/12.2536991 ◽

2020 ◽

Author(s):

Qi Li ◽

Liang Hong ◽

Huiling Sun

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Rural Settlements ◽

Remote Sensing Imagery

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Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽

10.3390/f10111047 ◽

2019 ◽

Vol 10 (11) ◽

pp. 1047 ◽

Cited By ~ 5

Author(s):

Ying Sun ◽

Jianfeng Huang ◽

Zurui Ao ◽

Dazhao Lao ◽

Qinchuan Xin

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Species Diversity ◽

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Diversity Index ◽

Tree Species Diversity ◽

Individual Tree ◽

Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

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