Classification method of cultivated land based on UAV visible light remote sensing

Abstract

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture. UAV remote sensing has the advantages of low cost use, simple operation, real-time acquisition of remote sensor images and high ground resolution. It is difficult to separate cultivated land from other terrain by using only a single feature, making it necessary to extract cultivated land by combining various features and hierarchical classification. In this study, the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information, shape information and position information of farmland. Based on the vegetation index, texture information and shape information in the visible light band, the object-oriented method was used to study the best scheme for extracting cultivated land area. After repeated experiments, it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters. Uncultivated crops and other features are separated by using the band information and texture information. The overall accuracy of this method is 86.40% and the Kappa coefficient is 0.80. The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision. However, there are some cases where the finely divided plots are misleading, so further optimization and improvement are needed.

Methodology for automated classification of farmland based on Earth remote sensing data

“…The method of automated classification is based on changing the reflection of vegetation cover in the red and near infrared regions of the electromagnetic spectrum is closely related to its green Phyto mass. To quantify the state of vegetation, the so-called normalized difference vegetation index NDVI (Normalized Difference Vegetation Index) was applied [2]. The index is calculated as the difference between the reflection values in the near infrared and red regions of the spectrum, divided by their sum.…”

Aerial Identification of Fruit Maturity in Amazonian Palms via Plant-Canopy Modeling

“…Normalized green-black difference index [25] G 2 −RB G 2 +RB VARI Visible Atmospherically Resistant Index [24] G−R G+R−B RG black Green Ratio [26] R G GR black Green Ratio Index [16] G R…”

Identifying and Counting Tobacco Plants in Fragmented Terrains Based on Unmanned Aerial Vehicle Images in Beipanjiang, China

“…Remote sensing technology has advantages, such as large coverage, quick access to information, and low cost, thereby compensating for the shortcomings of human surveys, and has been widely used in estimating large-scale crop yield estimation and the gathering of planting area statistics. Remote sensing data sources range from medium resolution (Landsat) to high spatial resolution remote sensing satellite images (such as SPOT-5, China-Brazil Earth Resources Satellite 02 B, ZY-1 02C, ZY-3) [3][4][5][6][7]. Working methods include the following: optical images and synthetic aperture radar (SAR) [8], remote sensing platform research from high-altitude satellite remote sensing to low-altitude unmanned aerial vehicle (UAV) remote sensing [9][10][11], monitoring method research from pixel featurebased index calculation using statistical methods to object-oriented depth and machine learning methods and identifying studies from regions to individual tobacco plants [12,13].…”