A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area
Source abstract
Abstract
Remote sensing data plays an important role in classifying land use/land cover (LULC) information from various sensors having different spectral, spatial and temporal resolutions. The fusion of an optical image and a synthetic aperture radar (SAR) image is significant for the study of LULC change and simulation in cloudy mountain areas. This paper proposes a novel feature-level fusion framework, in which the Landsat operational land imager (OLI) images with different cloud covers, and a fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image are selected to conduct LULC classification experiments. We take the karst mountain in Chongqing as a study area, following which the features of the spectrum, texture, and space of the optical and SAR images are extracted, respectively, supplemented by the normalized difference vegetation index (NDVI), elevation, slope and other relevant information. Furthermore, the fused feature image is subjected to object-oriented multi-scale segmentation, subsequently, an improved support vector machine (SVM) model is used to conduct the experiment. The results showed that the proposed framework has the advantages of multi-source data feature fusion, high classification performance and can be applied in mountain areas. The overall accuracy (OA) was more than 85%, with the Kappa coefficient values of 0.845. In terms of forest, gardenland, water, and artificial surfaces, the precision of fusion image was higher compared to single data source. In addition, ALOS-2 data have a comparative advantage in the extraction of shrubland, water, and artificial surfaces. This work aims to provide a reference for selecting the suitable data and methods for LULC classification in cloudy mountain areas. When in cloudy mountain areas, the fusion features of images should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Advanced Image Fusion Techniques · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China
School of Economics and Management, Tongji University, Shanghai 200092, China
The Third Institute of Photogrammetry and Remote Sensing, Ministry of Natural Resources, Chengdu 610100, China
College of Geographical Science, Southwest University, Chongqing 400715, China
Funding
the National Key Research and Development Program of China
References (41)
Show source-provided references
1. Dry and wet snow cover mapping in mountain areas using SAR and optical remote sensing data10.1109/JSTARS.2017.2673409 ↗
2. Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery10.1016/j.isprsjprs.2018.04.001 ↗
3. Luo, K., Li, B., and Moiwo, J. (2018). Monitoring Land-Use/Land-Cover changes at a provincial large scale using an object-oriented technique and medium-resolution remote-sensing images. Remote Sens., 10.10.3390/rs10122012 ↗
4. Using an integrated remote sensing approach for identification of bedrock and alluvium along the Front Range mountains, Colorado10.1117/1.JRS.11.046009 ↗
5. Fuzzy neural net-work classification of global land cover from a 1° AVHRR data set10.1016/S0034-4257(98)00088-1 ↗
6. Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques10.1016/j.compag.2016.12.006 ↗
7. Land cover classification in Beijing using Landsat 8 image
8. Fast semisupervised classification using histogram-based density estimation for large-scale polarimetric SAR data10.1109/LGRS.2019.2910413 ↗
9. Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series10.1016/j.rse.2019.111277 ↗
10. Rice mapping using RADARSAT-2 dual- and quad-pol data in a complex land-use watershed: Cau River Basin (Vietnam)10.1109/JSTARS.2016.2586102 ↗
11. Tavares, P.A., Beltrão, N.E.S., Guimarães, U.S., and Teodoro, A.C. (2019). Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, Eastern Brazilian Amazon. Sensors, 19.10.3390/s19051140 ↗
12. Yin, J., Yang, J., and Zhang, Q. (2017). Assessment of GF-3 polarimetric SAR data for physical scattering mechanism analysis and terrain classification. Sensors, 17.10.3390/s17122785 ↗
13. Adaptive model-based classification of PolSAR data10.1109/TGRS.2018.2845944 ↗
14. Chatziantoniou, A., Psomiadis, E., and Petropoulos, G.P. (2017). Co-orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a Mediterranean setting based on machine learning. Remote Sens., 9.10.3390/rs9121259 ↗
15. Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data
16. Hütt, C., Koppe, W., Miao, Y., and Bareth, G. (2016). Best accuracy Land Use/Land Cover (LULC) classification to derive crop types using multitemporal, multisensor, and multi-polarization SAR satellite images. Remote Sens., 8.10.3390/rs8080684 ↗
17. A refined classification approach by integrating Landsat Operational Land Imager (OLI) and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area10.1080/01431161.2016.1176273 ↗
18. Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/land cover classification10.1007/s12524-016-0625-y ↗
19. A review of remote sensing image fusion methods10.1016/j.inffus.2016.03.003 ↗
20. Zhang, R., Zhou, Y., Luo, H., Wang, F., and Wang, S. (2017). Estimation and analysis of spatiotemporal dynamics of the net primary productivity integrating efficiency model with process model in Karst Area. Remote Sens., 9.10.3390/rs9050477 ↗
21. Monitoring vegetation cover in Chongqing between 2001 and 2010 using remote sensing data10.1007/s10661-017-6210-1 ↗
22. MODTRAN4-based atmospheric correction algorithm: FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes)
23. Mapping the spatial-temporal variability of tropical forests by ALOS-2 L-band SAR big data analysis10.1016/j.rse.2019.111372 ↗
24. A review of target decomposition theorems in radar polarimetry10.1109/36.485127 ↗
25. Freeman, A., and Durden, S.L. (1993, January 12). Three-component scattering model to describe polarimetric SAR data. Proceedings of the International Society for Optical Engineering, San Diego, CA, USA.10.1117/12.140618 ↗
26. Four-component scattering model for polarimetric SAR image decomposition10.1109/TGRS.2005.852084 ↗
27. An entropy based classification scheme for land applications of polarimetric SAR10.1109/36.551935 ↗
28. Radar target decomposition theorems
29. Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press, Taylor & Francis Group.
30. Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area10.1080/17538947.2011.625049 ↗
31. A genetic algorithm solution to the gram-schmidt image fusion10.1080/01431161.2019.1667553 ↗
32. An optimal monitoring model of desertification in naiman banner based on feature space utilizing landsat8 oli image10.1109/ACCESS.2019.2962909 ↗
33. Novel fusion method for SAR and optical images based on non-subsampled shearlet transform10.1080/01431161.2020.1723175 ↗
34. Open access to Earth land-cover map10.1038/514434c ↗
35. Support-vector networks10.1007/BF00994018 ↗
36. Devroye, L., Györfi, L., and Lugosi, G. (1996). Vapnik-Chervonenkis theory. A Probabilistic Theory of Pattern Recognition, Springer.10.1007/978-1-4612-0711-5 ↗
37. Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)10.1080/19475705.2017.1407368 ↗
38. Feature selection for hyperspectral data based on recursive support vector machines10.1080/01431160802609718 ↗
39. Proper orthogonal decomposition and its applications—Part I: Theory10.1006/jsvi.2001.4041 ↗
40. A comparison of canonical discriminant analysis and principal component analysis for spectral transformation
41. Remote sensing of soil salinity: Potentials and constraints10.1016/S0034-4257(02)00188-8 ↗