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.
Highlights
Using remote-sensing data to quickly and accurately obtain land use/land cover (LULC)information can provide the basic data for studying the spatio–temporal changes in land use andAppl
Wang [7] used the Landsat operational land imager (OLI) data to conduct LULC classification in Beijing, the results showed that compared with Landsat TM/ETM+ data, the new features of the Landsat OLI data are helpful in improving the accuracy of LULC classification
SARprincipal and optical images based on LULC classification features and relevant context features
Summary
Using remote-sensing data to quickly and accurately obtain land use/land cover (LULC)information can provide the basic data for studying the spatio–temporal changes in land use andAppl. Using remote-sensing data to quickly and accurately obtain land use/land cover (LULC). Information can provide the basic data for studying the spatio–temporal changes in land use and. Sci. 2020, 10, 2928 global change [1]. The complex terrain severely affects the optical and microwave images [3], there is a large difference in the effective light received by each pixel of the remote-sensing image [4]. This phenomenon has resulted in considerable difficulties in the extraction of mountain-surface information
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