CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data

Abstract

Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and hinders further improvements in classification results, due to the insufficiency of information provided by very high-spatial resolution images (VHSRIs). To be specific, the phenomenon of different objects with similar spectrum and the lack of topographic information (heights) are natural drawbacks of VHSRIs. Thus, multisource data steps into people’s sight and shows a promising future. Firstly, for data fusion, this paper proposed a standard normalized digital surface model (StdnDSM) method which was actually a digital elevation model derived from a digital terrain model (DTM) and digital surface model (DSM) to break through the bottleneck by fusing VHSRI and cloud points. It smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification. The fusion data then were utilized to perform multiresolution segmentation (MRS) and worked as training data for the CNN. Moreover, the grey-level co-occurrence matrix (GLCM) was introduced for a stratified MRS. Secondly, for data processing, the stratified MRS was more efficient than unstratified MRS, and its outcome result was theoretically more rational and explainable than traditional global segmentation. Eventually, classes of segmented polygons were determined by majority voting. Compared to pixel-based and traditional object-based classification methods, majority voting strategy has stronger robustness and avoids misclassifications caused by minor misclassified centre points. Experimental analysis results suggested that the proposed method was promising for object-based classification.

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

“…Figure 5 shows the frequency images of the line and polygon graphs, and mean and standard deviation values of the 2-D matrix generated using the reference dataset. Overall accuracy (OA) [53] and standard Kappa coefficient [54] were used for model assessment [55][56][57]. To statistically compare model performance with multiple datasets, Demšar [58] and Garcia and Herrera [59] suggested the Wilcoxon paired rank test and Friedman test, which are nonparametric.…”

Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

“…DL models possess evident advantages in performance as high-level features of large dataset can be fully excavated, based on which non-linear relationships can be represented sufficiently. As the most mature DL framework, convolutional neural networks (CNN) have been widely used in geoscience domain, such as scene classification [36], land-cover classification [37][38][39][40], lithological facies classification [41,42], functional zone division [43] and ground target detection [44][45][46]. In recent years, CNN-based methods has been applied in landslide-related domain, especially in landslide detection [47][48][49][50][51][52][53][54][55][56].…”

Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

“…Among them, the historical records and daily updated wide field of view data of GF-1 and GF-6 satellites are shared freely through China's National Space Administration's GEO platform to support the needs of global sustainable development, disaster prevention and mitigation, and climate change adaptation. The development of high-performance computing resources, such as GPU clusters and clouds in recent years, has further facilitated the application of deep learning networks to land-cover classification [41,42], semantic segmentation [43], and information extraction using remote sensing images [44,45]. Using deep neural networks, Balaniuk et al performed country-wide identification and classification of mines and tailings dams in Brazil from freely available Sentinel-2 satellite imagery [46].…”