Abstract: Abstract: The rice is the main crop in China. Based on the advantages of flexibility, high accuracy and short working cycle of the unmanned aerial vehicle (UAV), in this paper, we aim to establish a method for the investigation of rice planting area by UAV remote sensing image. The six-rotor UAV's camera image sensor is CMOS with FOV94. The focus is on infinity. The maximum single pixel is 4 000×3 000 pixels. The experimental region and verification region mainly included rice, tree, grassland, bare land, water body and buildings and so on. At first, the multiple images with overlapped region were obtained by UAV. The complete images of the experimental region and the verification region were obtained by Agisoft photoscan software. The image spatial resolution of the experimental region was 0.04 m and the verification region was 0.02 m. The multiresolution segmentation algorithm of eCognition Developer 9 software was used to segment the complete image of the experimental region and the verification region to obtain several objects and calculate the spectral, geometric, and texture features of each object. Using multiresolution segmentation algorithm to segment the image, the scale parameter of experimental region: scale=480, shape=0.1, compact=0.1, and the total number of objects after the segmentation were 880. The scale parameter of experimental region: scale=1 500, shape=0.1, compact=0.3, a total of 240 split object after segmentation. Subjects in the experimental region and the verification region were divided into training samples and verification samples. Training samples in the experimental region were used to extract characteristic indexes for identifying rice, binary logistic model training samples for identifying rice, and establishment and verification of characteristic indexes. The sample was used to test the race recognition model. The characteristics indexes of race identified in this study were shape index, red mean, red standard deviation Max.diff (maximum difference), GLCM contrast (gray-level co-occurrence matrix contrast) and GLCM dissimilarity(gray-level co-occurrence matrix contrast dissimilarity). The red mean was the index for distinguishing vegetation cover (rice, grassland, tree) and non-vegetation covered objects (bare land, water body, building); red standard deviation and Max.diff (maximum difference) value depicted the color change of rice and grassland; and the color of rice was relatively uniform, with uneven growth and relatively large changes in color. The color of rice and tree changes was small, but the border of the tree was fragmented, the shape index was larger, and the shape index can effectively distinguish between the color mean and the change of rice and tree body. The texture features reflected the slowly-changing or periodically changing structure and arrangement properties of the land surface. Rice, tree, grassland, bare land, water body, and building all had certain differences. It was reasonable to distinguish the six characteristic indexes from the test area and the verification area based on the mechanism analysis, including shape index, red mean, red standard deviation, Max.diff (maximum difference). Based on GLCM contrast (gray-level co-occurrence matrix contrast) and GLCM dissimilarity (gray-level co-occurrence matrix contrast dissimilarity), six characteristic indexes Logistic model of discriminating two classifications of rice land lots, experimental region’s identification of training sample set the correct rate was 100%, the correct rate of validation sample set was 97%, and the overall correct rate was 98%. When the correct rate of training sample set was 100%, the correct rate of validation sample set was 99%, the overall correct rate was 99% on the verification region. Based on the image pixel method, the area of rice was measured and the area error was less than 3.5% compared with the result of visual interpretation. This method had a good effect in identifying paddy fields and high accuracy in area estimation. Therefore, this study had some applicability to the use of UAV visible light remote sensing imagery to survey rice planting information, and had certain reference value for rice census. The results showed that the growth period of paddy rice in the wrongly classified rice lags behind and had not been closed yet. Therefore, this method identified the influence of paddy field growth period on paddy fields and carries out multiple surveys at different times to further improve the method of identifying paddy rice plots the effective way to correct rate.