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Hyperspectral remote sensing technology has been widely used in vegetation type mapping, and the integrated use of spectral and texture features of remote sensing data has become an effective way to improve the accuracy of vegetation classification, but the Hughes effect generated by the high-dimensional feature space composed of multi-scale texture features and hyperspectral features reduced the image classification accuracy, and the randomness of feature optimization algorithms such as genetic algorithm (GA) can also cause uncertainty in image classification accuracy. In order to solve those problems, a high-dimensional spectral-texture feature space was established by incorporating spectral features of ZY1 02D Advanced HyperSpectral Imager(AHSI) hyperspectral data and multi-scale texture features extracted from ZY1 02D Visible and Near Infrared Camera(VNIC) data at various window sizes and movement directions, covering the vicinity of Zongjia Town in Dulan County, Qinghai Province, China. Jeffries-Matusita(J-M) distance is employed to assess the sample separability between vegetation types of texture features derived from varying window sizes and movement directions. Based on a combination of feature optimization algorithms (GA algorithm, particle swarm optimization (PSO), generalized normal distribution optimization (GNDO), atom search algorithm (ASO) and marine predators algorithm (MPA)) and classification algorithm (random forest (RF)), GA-RF, PSO-RF, GNDO-RF, ASO-RF, and MPA-RF algorithms were proposed and applied to the vegetation type classification of the high-dimensional spectral-texture feature space. The results show that the texture features extracted from different window sizes and window movement directions showed maximum J-M distance between distinct vegetations. For instance, the J-M distance between wolfberry (old) and wolfberry (new) is highest in the 3×3 window size texture images, while the J-M distance between wolfberry (old) and haloxylon is highest in the 11×11 window size texture images. In the 0° window movement direction texture images, poplar and haloxylon had the best separability to other vegetation types with J-M distances greater than 1.87 and 1.86, respectively. Grass and other vegetation types achieved the best separability with J-M distances greater than 1.18 in the 90° window movement direction texture images. The inclusion of multiscale texture features improved the overall classification accuracy (OA) by 8.02 percentage. The proposed GA-RF, PSO-RF, GNDO-RF, ASO-RF, and MPA-RF algorithms, when compared to the conventional random forest algorithm, led to an improvement in OA of vegetation classification by 1.32 to 2.41 percentage. Among them, the MPA-RF method exhibited the highest accuracy, achieving an OA and Kappa coefficient of 88.92% and 0.86, respectively. The results of this study demonstrate that texture images extracted from different window sizes and window movement directions are useful in distinguishing between different types of vegetation. The accuracy of vegetation recognition is significantly increased by adding multi-scale texture features to the spectral feature set. The combination of feature optimization algorithm and image classification algorithm by iterative optimization alleviates the randomness of the optimization algorithm results, overcomes the Hughes effect of high-dimensional features, and successfully improves the vegetation classification accuracy. This study proposes innovative ideas for feature extraction, feature optimization, and classification algorithm selection for hyperspectral vegetation classification studies. [ABSTRACT FROM AUTHOR]

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With the continuous development of hyperspectral remote sensing technology, it has been widely used in vegetation mapping. However, sparse vegetation canopy, soil background, and spectral similarity between different types of vegetation are still the main challenges for vegetation types mapping in arid areas. As a result, it is difficult to achieve reliable classification accuracy by using spectral or texture features separately. Generalized Normal Distribution Optimization (GNDO) is a new feature optimization algorithm, with advantages in quality and stability of feature extraction results, comparing to traditional optimization algorithms. But it has not yet been applied to select bands of hyperspectral data. In order to validate the feasibility of combining ZY-1 02D spectral and texture features to classify vegetation types in arid areas, to verify the effectiveness of the GNDO method for bands selection of hyperspectral data, and to explore the effects of feature selection methods and training sample numbers on the classification accuracy of vegetation mapping, different Wrapper Optimization methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and GNDO, were applied to select spectral features for vegetation mapping, taking the area around Zongjia Town, Dulan County, Qinghai Province, China as the research area, then the band selection results of these methods were analyzed. Train sample set containing 30, 50, 100, 150, and 200 pixels per class were used to select bands and to train the classifier. Different methods (ALL (without bands selection), GA, PSO, GWO, GNDO) and different sizes of the trained sample sets (30, 50, 100, 150, and 200 pixels per class) were used to obtain 25 spectral feature-based classification data sets. Simultaneously, 8 texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment) were extracted using the Gray-level co-occurrence matrix (GLCM) method, and selected on basis of distinguishability for vegetation types. Texture features (TEX) were combined with spectral feature-based classification data sets. The random forest classification method was applied to classify vegetation types for the classification data sets, and the classification accuracy of classification data sets was evaluated and compared. The results show that 1) the blue region (400-450 nm), the red edge region (700-750 nm), and the red region (600-650 nm) are the most sensitive to distinguish the vegetation types in the study area; 2) the GNDO200 achieved the highest overall classification accuracy (80.44%) among the spectral feature-based classification data sets, which was better than the classification accuracy (78.86%) using all bands (ALL200); 3) with the increase of training samples, the overall classification accuracy of each classification data set showed an increasing trend, the classification accuracy of different feature selection methods showed different reliance on the number of training samples; 4) image texture features significantly improved the classification accuracy, and the GWO200+TEX dataset had the highest overall classification accuracy (82.86%). This study could verify the potential of the ZY1-02D, the new hyperspectral satellite data, for the classification of vegetation types in arid areas, and provide an idea for the selection of spectral and texture features in hyperspectral vegetation mapping. [ABSTRACT FROM AUTHOR]