Robust Haze Reduction: An Integral Processing Component in Satellite-Based Land Cover Mapping

2002 ◽  
Author(s):  
B Guindon ◽  
Y Zhang
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Processing Component ◽  
Integral Processing

Optical and Radar Data Analysis for Land Use Land Cover Mapping in Peru

2019 ◽  
Vol 3 (1) ◽  
pp. 14-27
Author(s):  
Barry Haack ◽  
Ron Mahabir
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Window Size ◽  
Radar Data ◽  
Optical Data ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Texture Image ◽  
Site Class ◽  
Spectral Signatures

This analysis determined the best individual band and combinations of various numbers of bands for land use land cover mapping for three sites in Peru. The data included Landsat Thematic Mapper (TM) optical data, PALSAR L-band dual-polarized radar, and derived radar texture images. Spectral signatures were first obtained for each site class and separability between classes determined using divergence measures. Results show that the best single band for analysis was a TM band, which was different for each site. For two of the three sites, the second best band was a radar texture image from a large window size. For all sites the best three bands included two TM bands and a radar texture image. The original PALSAR bands were of limited value. Finally upon further analysis it was determined that no more than six bands were needed for viable classification at each study site.

scholarly journals Land cover mapping with ultra‐high‐resolution aerial imagery

2020 ◽  
Vol 6 (4) ◽  
pp. 429-430 ◽  
Author(s):  
Ned Horning
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Aerial Imagery ◽  
Land Cover Mapping

scholarly journals Graph Convolutional Networks-based Super-resolution Land Cover Mapping

2021 ◽  
pp. 1-1
Author(s):  
Xining Zhang ◽  
Yong Ge ◽  
Feng Ling ◽  
Jin Chen ◽  
Yuehong Chen ◽  
...  
Keyword(s):  
Land Cover ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Convolutional Networks

scholarly journals Global Land Cover Mapping with Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest

2021 ◽  
pp. 1-1
Author(s):  
Caleb Robinson ◽  
Kolya Malkin ◽  
Nebojsa Jojic ◽  
Huijun Chen ◽  
Rongjun Qin ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Land Cover Mapping ◽  
Weak Supervision ◽  
Global Land Cover ◽  
Global Land

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

scholarly journals Report on the 2020 IEEE GRSS Data Fusion Contest-Global Land Cover Mapping With Weak Supervision [Technical Committees]

2020 ◽  
Vol 8 (4) ◽  
pp. 134-137
Author(s):  
Naoto Yokoya ◽  
Pedram Ghamisi ◽  
Ronny Hansch ◽  
Michael Schmitt
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Land Cover Mapping ◽  
Weak Supervision ◽  
Global Land Cover ◽  
Global Land
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