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It was difficult to accurately obtain crop planting structure by using the spectral information of high spatial resolution and low spatial resolution multispectral images of panchromatic images at the same time. In this paper, we propose a method of planting structure extraction based on indices and an improved U-Net semantic segmentation network. Based on the original band of Landsat-8, we used an image fusion algorithm to highlight the characteristics of vegetation, water, and soil respectively by three indices added, and the improved U-Net network was used to classify the type of planting structure. The results showed that the overall accuracy of classification was more than 91.6%, and the accuracy of crops was up to 93.8%. Automated water extraction index in image fusion effectively improved the classification accuracy. This method could extract a variety of information about planting structures automatically and accurately. It provided theoretical support for adjusting and optimizing regional planting structures.

This paper proposes a method for extracting crop planting structures based on indexes, image fusions and an improved U-Net deep semantic segmentation network; this method can automatically and accurately extract multiple types of information regarding crop planting structures. Taking the Landsat 8 commercial multispectral satellite dataset as an example, three indexes are used to highlight the characteristics of vegetation, water and soil. The GS method is used to combine the indexes to fuse panchromatic bands and obtain 15-m high-resolution fused images, and the images are sliced to obtain training sets and test sets of images. When using different combinations of index-fused images and the improved U-Net network to classify the test set data, the overall accuracy rate reaches more than 92.4%, and the accuracy rate of the crop planting structure (wheat, cotton, and woodland) reaches up to 94.2%. The results showed that the participation of AWEI in the image synthesis process can significantly improve the classification effect of the crop planting structure by more than 1.2%, and the kappa coefficient of the NDVI-AWEI method reaches 0.886; thus, this method is the most stable and suitable for actual production. This method can assist relevant enterprises in adjusting and optimizing their regional planting structures, in the intelligent allocation of irrigation water and in other related decisions.

This paper takes Zhengzhou City, Henan Province as the research area, NLD (Night Light Data) high-resolution remote sensing image of 2017 as the data source. Two different supervised algorithms (Support Vector Machine & Deep Learning) was used for classification. During Deep learning, two kinds of semantic segmentation network models are selected: FCN (Full Convolution Neural Network) model, and U-Net model to classify source data and analyze the effects of different semantic segmentation networks on classification accuracy. We calculate the urban area of 460.34 square kilometers, 447.28 square kilometers, and 452.57 square kilometers by SVM (Support Vector Machine) algorithm, U-Net model and FCN model, while the urban area of 437.60 square kilometers in 2018 was announced by Zhengzhou Municipal Bureau of Statistics. The results showed that the classification accuracy of the SVM algorithm is 95.06%, the U-Net model reached 97.83%, and the FCN model had 96.69%, under the same conditions and similar spectral information. We found that the U-Net network model can get better classification results in areas with serious mixed features, both the semantic segmentation network models of the deep learning algorithm are more accurate than the SVM algorithm to the data released by the bureau of a statistic of Zhengzhou.