BTS: a binary tree sampling strategy for object identification based on deep learning

Object-based convolutional neural networks (OCNNs) have achieved great performance in the field of land-cover and land-use classification. Studies have suggested that the generation of object convo...

[1]  Zong‐Liang Yang,et al.  Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation , 2018, Remote Sensing of Environment.

[2]  Xin Huang,et al.  An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images , 2020 .

[3]  Zhenfeng Shao,et al.  Remote Sensing Image Fusion With Deep Convolutional Neural Network , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Tong Zhang,et al.  Deep Learning Based Feature Selection for Remote Sensing Scene Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[6]  Cong Xu,et al.  Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Yaolin Liu,et al.  Characterizing land-use classes in remote sensing imagery by shape metrics , 2012 .

[8]  Neil Flood,et al.  Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Shihong Du,et al.  Learning multiscale and deep representations for classifying remotely sensed imagery , 2016 .

[10]  Xueliang Zhang,et al.  Deep learning in remote sensing applications: A meta-analysis and review , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[11]  Gui-Song Xia,et al.  Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models , 2018 .

[12]  Zhou Guo,et al.  Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds , 2018, Int. J. Geogr. Inf. Sci..

[13]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[15]  Q. Liu,et al.  On-board radiometric calibration for thermal emission band of FY-3C/MERSI , 2019 .

[16]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[17]  R. O'Neill,et al.  Landscape patterns in a disturbed environment , 1987 .

[18]  Hugh G. Lewis,et al.  Determination of spatial and temporal characteristics as an aid to neural network cloud classification , 1997 .

[19]  Xiuwen Liu,et al.  A patch-based convolutional neural network for remote sensing image classification , 2017, Neural Networks.

[20]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[21]  Meiling Liu,et al.  Detection of Rice Phenological Variations under Heavy Metal Stress by Means of Blended Landsat and MODIS Image Time Series , 2018, Remote. Sens..

[22]  Dongping Ming,et al.  SO–CNN based urban functional zone fine division with VHR remote sensing image , 2020 .

[23]  Peng Yue,et al.  A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Junjun Jiang,et al.  Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network , 2019, Remote. Sens..

[25]  Zhou Guo,et al.  On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery , 2015 .

[26]  Xia Li,et al.  Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata , 2020, Int. J. Geogr. Inf. Sci..

[27]  Rangasami L. Kashyap,et al.  Multiresolution segmentation-based image coding with hierarchical data structures , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[28]  Brian K. Gelder,et al.  Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution , 2020 .

[29]  Jie Wang,et al.  Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Peter M. Atkinson,et al.  Novel shape indices for vector landscape pattern analysis , 2016, Int. J. Geogr. Inf. Sci..

[31]  Ashok Samal,et al.  A simple method for fitting of bounding rectangle to closed regions , 2007, Pattern Recognit..

[32]  Liangpei Zhang,et al.  Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Dongping Ming,et al.  Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation , 2019, Earth Science Informatics.

[34]  Min Wang,et al.  A New Method for Region-Based Majority Voting CNNs for Very High Resolution Image Classification , 2018, Remote. Sens..

[35]  Manchun Li,et al.  Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery , 2018 .