[Objective] Based on the PIE SDK, Landsat-8 was used as the data source to realize the plug-in corn planting area extraction tool, which provided technical support for the rapid and objective acquisition of county corn planting area information, and assisted agricultural remote sensing application and agricultural development. [Methods] The PIE SDK was used for plug-in secondary development to realize the radiometric calibration, fusion and cropping functions of remote sensing images. The vegetation distribution of the experimental area was obtained by normalized vegetation index (NDVI), and the K-means classification method was used to realize Corn planting area extraction. [Results] Choosing Weishi County as experimental area，according to the Landsat-8/OLI data of September 4, 2014, the corn planting area was 29,800 hectares, accounting for 23.5% of the total area of the experimental area, mainly distributed in the central and eastern parts of the experimental area. (1) The corn planting area extracted by the development plug-in was 29,800 hectares. In 2014, the corn planting area in Weishi County was about 27,800 hectares. The total area error of the two compared with the experimental area was 2.25%. The high quality provides an effective tool for the survey of corn planting area in Weishi County. (2) The distribution map of corn plantation in Weishi County was obtained by processing the classification results, which is basically consistent with the distribution of corn planting in the county over the years. The method of extracting corn planting area in Weishi County by using NDVI and K-means method It is feasible, and the experimental results show that the maximum number of iterations is set to 30. This method can provide reference for the threshold setting of corn planting area information in the county. [Conclusions] Based on the PIE SDK for secondary development, to achieve the extraction of corn planting area in the county. The results basically meet the statistical data quality requirements, and also provide a reference for the plug-in development of similar crop extraction tools based on the PIE SDK, and can provide objective data to support fast and accurate information on the cultivation statistics, subsidies and insurance business as corn.
Remote sensing image classification has important research significance and application value in image information extraction, ground object detection and identification, and is widely used in military reconnaissance, disaster relief, crop recognition and yield estimation and other military and civil fields. In the past few decades, scholars have done a lot of research on remote sensing image classification, and put forward multiple classification methods, which are mainly divided into supervised classification and unsupervised classification. However, with the increasement of remote sensing image resolution, traditional classification algorithms can not meet the needs for high-precision classification, and also unable to solve “the different objects with same spectrum” and “the same object with different spectrum” problem. In recent years, machine learning has made breakthroughs in image classification research. As a branch of machine learning, deep learning stands out among many machine algorithms for its applicability of learning models and accuracy of classification results. Therefore, more and more scholars apply deep learning to remote sensing image classification. In this paper, the application of deep learning in remote sensing image classification is analyzed and prospected. Firstly, the basic process of classification is summarized, and the common data sets are introduced. Secondly, frequently-used models and open source tools in application has been introduced, with the analysis of the latest application progress in rapidly developing deep learning methods. Finally, the difficulties and challenges existing in the application is discussed and the trend is prospected.
We apply the semantic segmentation method in deep network to high precision satellite image change detection, and propose a network framework to improve the detection performance.We directly processed the image after registration, without the steps of radiometric correction, and avoided the tedious steps of manual feature design by traditional methods.We tried to use Unet and Deeplab v3 model to divide the change area, and added the structure of jumping connection on the basis of Deeplab network, which made the edge of the detection graph more accurate and improved the performance of the network.The test results show that this method is effective for detecting the change of highprecision remote sensing images.
Semantic segmentation is one of the basic themes in computer vision. Its purpose is to assign semantic tags to each pixel of an image, which has been applied in many fields such as medical field, intelligent transportation and remote sensing image. In this paper, we use deep learning to solve the task of remote sensing semantic image segmentation. We propose an algorithm for semantic segmentation of the Attention Seg-Net network combined with SegNet and attention gate. Our proposed network can better segment vegetation, buildings, water bodies and roads in the test set of remote sensing images.