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Xiaoxue Feng
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, 12465 Beijing, China, 100871

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Journal article
Published: 24 June 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Detection of urban land expansion is important for understanding urbanization process and improving urban planning. Spatio-temporal contextual information derived from multitemporal high resolution imagery is useful for highlighting urban land cover changes. This paper proposed a new method for detecting urban built-up area change from multitemporal high spatial resolution imagery by combining spectral and spatio-temporal features. A multi-band temporal texture measured using pseudo cross multivariate variogram (PCMV) was adopted to quantify the local spatio-temporal dependence between bi-temporal multispectral images. The PCMV textures at multiple scales, bi-temporal spectral features and normalized difference vegetation indices (NDVIs) were together input to an improved One-class Random Forest (iOCRF) classifier for urban built-up area change mapping. The proposed method was evaluated in urban built-up area change detection using multitemporal Sentinel-2 images of Tianjin area acquired from 2015 to 2019. It was also compared with three feature combinations and an existing post-classification comparison method based on One-class Support Vector Machine (OCSVM). Experimental results demonstrated that the proposed method outperformed the traditional ones, with increases of 2.157.38%, 2.075.45%, 1.936.76% and 5.9813.11% in overall accuracy. Moreover, the proposed method also achieved the best performance using the bi-temporal Sentinel-2 images over the east of Beijing area. The proposed method is promising as a simple and reliable way to detect urban built-up area change with multitemporal Sentinel-2 imagery.

ACS Style

Xiaoxue Feng; Peijun Li; Tao Cheng. Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 6974 -6986.

AMA Style

Xiaoxue Feng, Peijun Li, Tao Cheng. Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):6974-6986.

Chicago/Turabian Style

Xiaoxue Feng; Peijun Li; Tao Cheng. 2021. "Detection of Urban Built-Up Area Change From Sentinel-2 Images Using Multiband Temporal Texture and One-Class Random Forest." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 6974-6986.

Journal article
Published: 01 November 2019 in Remote Sensing
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High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method.

ACS Style

Runzhi Wang; Wenhui Wan; Kaichang Di; Ruilin Chen; Xiaoxue Feng. A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction. Remote Sensing 2019, 11, 2572 .

AMA Style

Runzhi Wang, Wenhui Wan, Kaichang Di, Ruilin Chen, Xiaoxue Feng. A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction. Remote Sensing. 2019; 11 (21):2572.

Chicago/Turabian Style

Runzhi Wang; Wenhui Wan; Kaichang Di; Ruilin Chen; Xiaoxue Feng. 2019. "A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction." Remote Sensing 11, no. 21: 2572.

Journal article
Published: 22 August 2019 in Remote Sensing
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Timely and accurate information about spatial distribution of tree species in urban areas provides crucial data for sustainable urban development, management and planning. Very high spatial resolution data collected by sensors onboard Unmanned Aerial Vehicles (UAV) systems provide rich data sources for mapping tree species. This paper proposes a method of tree species mapping from UAV images over urban areas using similarity in tree-crown object histograms and a simple thresholding method. Tree-crown objects are first extracted and used as processing units in subsequent steps. Tree-crown object histograms of multiple features, i.e., spectral and height related features, are generated to quantify within-object variability. A specific tree species is extracted by comparing similarity in histogram between a target tree-crown object and reference objects. The proposed method is evaluated in mapping four different tree species using UAV multispectral ortho-images and derived Digital Surface Model (DSM) data collected in Shanghai urban area, by comparing with an existing method. The results demonstrate that the proposed method outperforms the comparative method for all four tree species, with improvements of 0.61–5.81% in overall accuracy. The proposed method provides a simple and effective way of mapping tree species over urban area.

ACS Style

Xiaoxue Feng; Peijun Li. A Tree Species Mapping Method from UAV Images over Urban Area Using Similarity in Tree-Crown Object Histograms. Remote Sensing 2019, 11, 1982 .

AMA Style

Xiaoxue Feng, Peijun Li. A Tree Species Mapping Method from UAV Images over Urban Area Using Similarity in Tree-Crown Object Histograms. Remote Sensing. 2019; 11 (17):1982.

Chicago/Turabian Style

Xiaoxue Feng; Peijun Li. 2019. "A Tree Species Mapping Method from UAV Images over Urban Area Using Similarity in Tree-Crown Object Histograms." Remote Sensing 11, no. 17: 1982.