No abstract

Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

Human-vehicle classification plays an important role in advanced driver assistance systems (ADAS). The use of millimeter wave (mmWave) radar sensor in human-vehicle classification algorithms is of great significance since the sensor maintains to be robust in severe weather (e.g. fog, snow, etc.). To improve classification accuracy under complex scenes of autonomous driving, a new mmWave radar point cloud classification algorithm is proposed in this paper, which realizes human-vehicle classification employing a newly proposed point cloud feature vector with eleven dimensions and based on kernel support vector machine (SVM) classifier. To verify the validity and robustness of the proposed feature vector, a 77 GHz radar is used to collect two datasets for static and moving objects, respectively, with each dataset taken for pedestrians and vehicles at different distances and angles. Experimental results show that the proposed algorithm achieves higher classification accuracy than a conventional one based on signal features. For the comparison based on the same number of dimensions, the number of dimensions of the proposed feature vector is decreased by removing the features with low significance. Experimental results verify that the proposed algorithm maintains advantage over the conventional one.

Zhang (2019) Simulating and forecasting spatio-temporal characteristic of land-use/ cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China,

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.