The Use of Indices and Modified U-Net Network in Improving the Classification of Planting Structures.

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. [ABSTRACT FROM AUTHOR]