Abstract
Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem. The main form of land cover is different types of soil. Compared with traditional methods, visible and near-infrared spectroscopy technology can classify different types of soil rapidly, effectively, and nondestructively. Based on the visible near-infrared spectroscopy technology, this paper takes the soil of six different land cover types in Qingdao, China orchards, woodlands, tea plantations, farmlands, bare lands, and grasslands as examples and establishes a convolutional neural network classification model. The classification results of different number of training samples are analyzed and compared with the support vector machine algorithm. Under the condition that Kennard–Stone algorithm divides the calibration set, the classification results of six different soil types and single six soil types by convolutional neural network are better than those by the support vector machine. Under the condition of randomly dividing the calibration set according to the proportion of 1/3 and 1/4, the classification results by convolutional neural network are also better. The aim of this study is to analyze the feasibility of land cover classification with small samples by convolutional neural network and, according to the deep learning algorithm, to explore new methods for rapid, nondestructive, and accurate classification of the land cover.
Highlights
Land cover is a direct result of the interaction between natural environment and human activities
Changes in land cover will cause the changes in the climate and environmental characteristics, which has an important influence on the social economy and ecosystem [1, 2]. e main form of land cover is different types of soil, including cultivated lands, woodlands, grasslands, and bare lands. erefore, it is of great significance to classify different types of soil quickly and accurately for land cover research, soil investigation, and mapping
When the total number of the calibration set was from 90 to 40, the classification result of 90 calibration set samples is 98.89%, and the rest was 100%. e classification accuracy of more than 60 labeled samples in the test set is more than 98%
Summary
Land cover is a direct result of the interaction between natural environment and human activities. It mainly focuses on describing the natural properties of the earth’s surface which has specific time and space characteristics. The classification technology of the remote sensing image is mostly used to realize the classification of different types of soil [3,4,5]. Visible and near-infrared spectroscopy technology is a fast, nondestructive measurement method. It has been widely used in medicine, agriculture, oil, and other fields [6,7,8,9]. It has been widely used in medicine, agriculture, oil, and other fields [6,7,8,9]. e spectral analysis method indirectly obtains useful information of the substance
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