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.

As one of the most important active remote sensing technologies, synthetic aperture radar (SAR) provides advanced advantages of all-day, all-weather, and strong penetration capabilities. Due to its unique electromagnetic spectrum and imaging mechanism, the dimensions of remote sensing data have been considerably expanded. Important for fundamental research in microwave remote sensing, SAR image classification has been proven to have great value in many remote sensing applications. Many widely used SAR image classification algorithms rely on the combination of hand-designed features and machine learning classifiers, which still experience many issues that remain to be resolved and overcome, including optimized feature representation, the fuzzy confusion of speckle noise, the widespread applicability, and so on. To mitigate some of the issues and to improve the pattern recognition of high-resolution SAR images, a ConvCRF model combined with superpixel boundary constraint is developed. The proposed algorithm can successfully combine the local and global advantages of fully connected conditional random fields and deep models. An optimizing strategy using a superpixel boundary constraint in the inference iterations more efficiently preserves structure details. The experimental results demonstrate that the proposed method provides competitive advantages over other widely used models. In the land cover classification experiments using the MSTAR, E-SAR and GF-3 datasets, the overall accuracy of our proposed method achieves 90.18 ± 0.37, 91.63 ± 0.27, and 90.91 ± 0.31, respectively. Regarding the issues of SAR image classification, a novel integrated learning containing local and global image features can bring practical implications.