Integrating Zhuhai-1 Hyperspectral Imagery With Sentinel-2 Multispectral Imagery to Improve High-Resolution Impervious Surface Area Mapping

Abstract

Mapping impervious surface area (ISA) in an accurate and timely manner is essential for a variety of fields and applications, such as urban heat islands, hydrology, waterlogging, and urban planning and management. However, the large and complex urban landscapes pose great challenges in retrieving ISA information. Spaceborne hyperspectral (HS) remote sensing imagery provides rich spectral information with short revisit cycles, making it an ideal data source for ISA extraction from complex urban scenes. Nevertheless, insufficient single-band energy, the involvement of modulation transfer function (MTF), and the low signal-to-noise ratio (SNR) of spaceborne HS imagery usually result in poor image clarity and noises, leading to inaccurate ISA extraction. To address this challenge, we propose a new deep feature fusion-based classification method to improve 10 m resolution ISA mapping by integrating Zhuhai-1 HS imagery with Sentinel-2 multispectral (MS) imagery. We extract deep features that include spectral and spatial features respectively from MS and HS imagery via a 2D convolutional neural network (CNN), aiming to increase feature diversity and improve the model's recognition capability. The Sentinel-2 imagery is used to enhance the spatial information of the Zhuhai-1 HS image, improving the urban ISA retrieval by reducing the impact of noises. By combining the deep spatial features and deep spectral features, we obtain joint spatial-spectral features, leading to high classification accuracy and robustness. We test the proposed method in two highly urbanized study areas that cover Foshan city and Wuhan city, China. The results reveal that the proposed method obtains an overall accuracy of 96.72% and 96.75% in the two study areas, 18.78% and 8.66% higher than classification results with only HS imagery as input. The final ISA extraction overall accuracy is 95.42% and 95.50% in the two study areas, the highest among the comparison methods.

Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

“…A total of 21 features were calculated and fed to the cl sifier for ISA Mapping, including 3 spectral index features (NDVI, NDBI, MNDWI GLCM texture features, 10 land surface reflectance features, and 2 backscattering featur 3.2.1. Spectral Indices Feature Early studies indicated that combining spectral indices features is conductive to provement of accuracy in ISA mapping [38,39]. In this study, the following three spec indices features were calculated, including the Normalized Difference Vegetation Ind (NDVI), which can highlight vegetated land [40]; the Modified Normalized Differe Water Index (MNDWI), which has a good effect on distinguishing water bodies from la use/cover [41]; and the Normalized Difference Built-up Index (NDBI), which has a sign icant effect on the extraction of ISA [42].…”

“…Early studies indicated that combining spectral indices features is conductive to improvement of accuracy in ISA mapping [38,39]. In this study, the following three spectral indices features were calculated, including the Normalized Difference Vegetation Index (NDVI), which can highlight vegetated land [40]; the Modified Normalized Difference Water Index (MNDWI), which has a good effect on distinguishing water bodies from land use/cover [41]; and the Normalized Difference Built-up Index (NDBI), which has a significant effect on the extraction of ISA [42].…”

Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism

“…Hyperspectral image classification (HSIC) aims to assign a unique category identity to each element in the image, which is a key technology for the intelligent interpretation of the hyperspectral image (HSI) and has been widely used in many fields such as urban development planning [1,2], agricultural land use [3][4][5], military target detection [6,7] and medical pathological diagnosis [8,9]. However, the problems of low spatial resolution, high spectral dimensionality and lack of labelled samples in HSI pose great challenges to the classification task [10][11][12].…”

Remote Sensing of Poplar Phenophase and Leaf Miner Attack in Urban Forests

“…These subtle changes are rather identified by high-resolution hyperspectral imaging of the canopies where both changes in spectra and the texture appear [34]. Other researchers also found that hyperspectral cameras have great potential in detecting insect disturbances [35] but others highlighted that the best results are reaped when fusing spectral information from hyperspectral cameras with spatial information from satellite data [36]. Therefore, the aim of this study is to determine the best vegetation indices used for the recognition of defoliation as well as the type and position of the sensors.…”