The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images

Abstract

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a significant role in the fine classification of vegetation types. However, it is not clear how the ultrahigh resolution UAV hyperspectral images react in the fine classification of vegetation types in highly fragmented planting areas, and how the spatial resolution variation of UAV images will affect the classification accuracy. Based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (S185) onboard a UAV platform, this paper examines the impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas in southern China by aggregating 0.025 m hyperspectral image to relatively coarse spatial resolutions (0.05, 0.1, 0.25, 0.5, 1, 2.5 m). The object-based image analysis (OBIA) method was used and the effects of several segmentation scale parameters and different number of features were discussed. Finally, the classification accuracies from 84.3% to 91.3% were obtained successfully for multi-scale images. The results show that with the decrease of spatial resolution, the classification accuracies show a stable and slight fluctuation and then gradually decrease since the 0.5 m spatial resolution. The best classification accuracy does not occur in the original image, but at an intermediate level of resolution. The study also proves that the appropriate feature parameters vary at different scales. With the decrease of spatial resolution, the importance of vegetation index features has increased, and that of textural features shows an opposite trend; the appropriate segmentation scale has gradually decreased, and the appropriate number of features is 30 to 40. Therefore, it is of vital importance to select appropriate feature parameters for images in different scales so as to ensure the accuracy of classification.

The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

“…Regarding the spatial resolution, it does not appear that the coarser resolution of PRISMA has negatively affected the separability of the classes. As found by Ghosh et al [ 54 ] and Liu et al [ 55 ] a finer spatial resolution is not necessarily better. The former authors obtained better classification accuracy using Hyperion hyperspectral imagery at 30 m spatial resolution than HyMAP imagery at 8 m resolution.…”

Mapping Wetland Plant Communities Using Unmanned Aerial Vehicle Hyperspectral Imagery by Comparing Object/Pixel-Based Classifications Combining Multiple Machine-Learning Algorithms

“…The high dimensionality of the hyperspectral data makes the classification problem more complex. High-dimensional data usually need feature selection before machine learning [41]. The feature selection was aimed at reducing processing times and developing ways to use less of the data but still be able to achieve satisfying results.…”

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

“…For example, previous work has demonstrated that low spatial image resolution has a substantial negative effect on the accuracy of measuring plant ground cover from images [20]. Extracting textural features from aerial images for vegetation classification also requires high spatial resolution [21]. It has also been shown that the accuracy of detecting and localizing objects in images is highly dependent on image resolution [22,23].…”

“…High-resolution sensors are expensive and heavier and must be flown on larger and more expensive UAVs with higher payload capacity. For crop imaging, multispectral sensors are often used, which is the trade-off spatial resolution for spectral resolution [21]. Even if a small GSD is obtained by optimizing sensor and flight parameters, during a flight, atmospheric disturbances and wind can cause image blur and substantially reduce the effective spatial resolution of the acquired images.…”