A novel approach to land-cover maps updating in complex scenarios based on multitemporal remote sensing images

2010 ◽  
Author(s):  
K. Bahirat ◽  
F. Bovolo ◽  
L. Bruzzone ◽  
S. Chaudhuri
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Remote Sensing Images ◽  
Novel Approach ◽  
Multitemporal Remote Sensing ◽  
Land Cover Maps

scholarly journals Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels

2020 ◽  
Vol 12 (3) ◽  
pp. 503
Author(s):  
Li ◽  
Chen ◽  
Foody ◽  
Wang ◽  
Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Spatial Resolution ◽  
Temporal Frequency ◽  
Land Cover Mapping ◽  
Remote Sensing Images ◽  
Ancillary Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Land Cover Maps

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

scholarly journals Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

2019 ◽  
Vol 11 (15) ◽  
pp. 1815 ◽  
Author(s):  
Jia ◽  
Ge ◽  
Chen ◽  
Li ◽  
Heuvelink ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Fine Scale ◽  
Remote Sensing Images ◽  
Resolution Mapping ◽  
Super Resolution Mapping ◽  
Land Cover Maps

Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects.

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