Applying Neural Network Classification to Obtain Mangrove Landscape Characteristics for Monitoring the Travel Environment Quality on the Beihai Coast of Guangxi, P. R. China

摘要

The spectral characteristics of mangroves on the Beihai Coast of Guangxi, P. R. China are acquired on the basis of spectral data from field measurements. Following this, the 3-layer reverse-conversing neural networks (NN) classification technology is used to analyze the Landsat TM5 image obtained on January 8, 2003. It is detailed enough to facilitate the introduction of the algorithm principle and trains project of the neural network. Neural network algorithms have characteristics including large-scale data handling and distributing information storage. This research firstly analyzes the necessity and complexity of this translation system, and then introduces the strong points of the neural network. Processing mangrove landscape characteristics by using neural network is an important innovation, with great theoretical and practical significance. This kind of neural network can greatly improve the classification accuracy. The spatial resolution of Landsat TM5 is high enough to facilitate the research, and the false color composite from 3-, 4-, and 5-bands has a clear boundary and provides a significant quantity of information and effective images. On the basis of a field survey, the exported layers are defined as mangrove, vegetation, bare land, wetlands and shrimp pool. TM satellite images are applied to false color composites by using 3-, 4-, and 5-bands, and then a supervised classification model is used to classify the image. The processing method of hyper-spectrum remote sensing allows the spectral characteristics of the mangrove to be determined, and integrates the result with the NN classification for the false color composite by using 3-, 4-, and 5-bands. The network model consists of three layers, i.e., the input layer, the hidden layer, and the output layer. The input layer number of classification is defined as 3, and the hidden layers are defined as 5 according to the function operation. The control threshold is 0.9. The training ratio is 0.2. The maximum permit error is 0.08. The classification precision reaches 86.86%. This is higher than the precision of maximal parallel classification (50.79%) and the spectrum angle classification (75.39%). The results include the uniformity ratio (1.7789), the assembly ratio (0.6854), the dominance ratio ( - 1.5850), and the fragmentation ratio (0.0325).