Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

Abstract

Abstract:The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m) to EO-1 Hyperion (with a spatial resolution of 30 m). The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC), pixel-based support vector machine (PSVM), hybrid maximum likelihood classifier (HMLC), and hybrid support vector machine (HSVM) were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the hybrid classifier.

Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

“…Continual progress in measurement techniques, image processing algorithms, development of computation models, new sensors and new satellite missions have brought new data and methods. Use of hyperspectral data in the erosion assessment represents a promising method, particularly in the context of the expectations forthcoming spaceborne hyperspectral sensors with high signal-to-noise ratio (SNR) and pixel size from one to several tens of metre, such as German Environmental Mapping and Analysis Program (EnMAP), Italian Hyperspectral Precursor of the Application Mission (PRISMA), NASA's Hyperspectral Infra-Red Imager (HyspIRI), Japanese Hyperspectral Imager Suite (HISUI), French HypXIM, israel-italian Spaceborne Hyperspectral Applicative Land and Ocean Mission (SHALOM) [15] or Chinese TianGong-1 [16].…”

Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD

“…In addition, we use a 'stratified k-fold' approach to divide the data into training and testing sets in the CV process. The number of k-folds is set to 5 as suggested in similar studies [19,61,62] and the grid is generated with the fixed seed value for reproducibility. We then used the grid in the 'Randomized Search CV' function to randomly sample a set of hyperparameter values and conduct a stratified k-fold CV with each combination of values.…”

Self-Organizing Maps for Clustering Hyperspectral Images On-Board a CubeSat

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