# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 1 Introduction  
Agricultural environments are known to be sensitive to abnormal weather conditions and climatic disasters such as drought and flood [1, 2], thus rendering essential systematic monitoring of crop conditions and crop yield forecasting [3, 4]. Remote sensing technology received attention in the agriculture community due to its ability to provide periodic and regional information for crop monitoring and thematic mapping [5, 6]. Crop type maps derived from classification of remote sensing images are important resources for crop yield estimation and forecasting. Since any error in the crop type maps affects outputs of crop yield and forecasting models, it is critical to generate reliable crop type maps [6]. The most important elements of input remote sensing images for crop classification are their spatial and temporal resolutions. Since each individual crop has its own growth cycle, time-series images are necessary to fully account for variations of physical characteristics that accompany crop growth [7, 8]. According tothe scale of the target area of interest, satellite images with proper spatial resolution should be used as input for crop classification. If coarse-resolution satellite images are used, mixed pixel problems are likely and classification performance decreases [9; 10]. This is a common issue in Korea, where various types of crops are cultivated in small areas. The use of high-resolution satellite images and aerial photos can contribute to resolving the mixed pixel issues [11; 12]. Despite the increased discrimination capability of high-resolution images, it is difficult to collect time-series datasets over the full growth cycles of crops. Acquisition of optical satellite images depends heavily on atmospheric conditions; thus, the images are often contaminated and masked by clouds. In addition, it is difficult to acquire time-series aerial photos at desired times due to cost issues. In recent years, there was a growing interest in imaging of unmanned aerial vehicles (UAV) [11; 12; 13; 14; 15]. The advantage of UAV images over satellite images is their ability to provide local thematic information with much higher spatial and temporal resolutions [15]. UAV images with ultra-high spatial resolution [16; 17] can improve the discrimination capability of various surface objects, leading to an increase in the number of detectable targets. Compared with satellite images, low-cost flexible control of unmanned aerial systems (UAS) enables easier acquisition of images at the desired times between sowing and harvesting of crops [12; 15; 16]. Despite the great potential of UAV imaging, the technique has several practical issues. Firstly, the ultra-high spatial resolution of UAV images usually causes noise effects due to increased detectable targets when conventional pixel-based approaches are applied for classification [11; 18; 19]. The common approach to mitigate noise effects is to either use spatial contextual information or apply an object-oriented classification approach. For the spatial contextual information approach, texture information is firstly extracted from a gray-level co-occurrence matrix (GLCM) [20] and then combined with spectral information for classification [21; 22; 23]. The utilization of such texture information can reduce the impacts of isolated pixels within the pixel-based approach. The object-oriented approach first extracts meaningful objects via multi-resolution segmentation [24] and classification is then carried out on object units [25; 26; 27]. These two approaches are known to achieve better classification accuracy than the pixel-based approach based purely on spectral information [19; 22]. The second issue is heavy computational load related to data preprocessing and processing [11]. Most UAV images are acquired using a narrow field-of-view, which requires mosaicking of many sub-images to obtain a complete image set. If the sub-images are taken at different solar conditions and flight altitudes, radiometric calibration should be employed during mosaicking.