Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images

Lv, Zhiyong; Liu, Tongfei; Benediktsson, Jón Atli

doi:10.1109/tgrs.2020.2977248

Cited by 54 publications

(33 citation statements)

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“…A deep belief network along with a logistic regression classifier were used by [30] to detect landslides on hyperspectral images. [31,32] used freely available high-resolution Google Earth TM images for scattered shrub detection with a CNN model. [33] and [34] applied the CNN model for dwelling detection in refugee camps from VHR imagery sources include Worldview imagery (0.3-0.5 m) in different studies.…”

Section: Landslide Mapping Using Two Main Deep-learning Convolution Nmentioning

confidence: 99%

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Ghorbanzadeh

Meena

Shahabi

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Landslide Mapping Using Two Main Deep-learning Convolution Nmentioning

confidence: 99%

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Ghorbanzadeh

Meena

Shahabi

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The history of land use mapping by remote sensing technology dates back to 1970s when the first satellite remote sensing for Earth observation has provided the unique opportunity for quantitative analysis and dynamic monitoring in urban land use [8,9]. Not until around 2000 has a variety of very-high-resolution (VHR) satellite sensors been available and shown a clearer representation of the ground truth [10][11][12][13]. More recently, plenty of virtual global platforms, such as Google Earth, NASA World Wind, and Bing Maps, have opened a new era of digital earth [14,15].…”

Section: Introductionmentioning

confidence: 99%

Identifying Urban Building Function by Integrating Remote Sensing Imagery and POI Data

Lin

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Identifying urban building function plays a critical role in understanding the complexness of urban construction and improving the effectiveness of urban planning. The emergence of user generated contents has brought access to massive semantic information which complements the traditional remote sensing data for identifying urban building functions and exploring the spatial structure in urban environment. This work proposes a stepwise identification framework for urban building functions based on remote sensing imagery and POI data, which merges the spatial similarity of buildings and kernel density to improve the identification accuracy and completeness. Taking Wuhan as an example, Google earth images and POI data were obtained to identify the seven primary categories for the individual buildings in the core urban area. The results suggest that the proposed stepwise framework is feasible to identify the urban building functions as the identification results exhibit the superiority in terms of accuracy and completeness. Our results suggest that the identification of urban building function is sensitive to the bandwidth of KDE and 200 meter is the optimal size. The findings also indicate that significant spatial agglomeration exists in residential and commercial buildings at both macro and micro levels.

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“…Numerous CD methods have been developed for high-resolution RS imagery, including image difference, image transformation, post-classification comparison, and object-oriented analysis (OOA) [10,11]. In particular, the very-high-resolution (VHR) images can provide rich spectral information at the cost of enlarging the spectral heterogeneity within image objects, which incurs more speckle noises [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

2021

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Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. 1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. 2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. 3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.

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Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images

Cited by 54 publications

References 50 publications

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model

Identifying Urban Building Function by Integrating Remote Sensing Imagery and POI Data

Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos

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