Identification and spatial pattern analysis of alpine timberline by remote sensing methods in Yarlung Zangpo Grand Canyon

As an important ecotone,the alpine timberline is the boundary between closed-canopy montane forest and alpine vegetation,and is highly sensitive to global and regional climate changes.We provided a way to identify and extract the alpine timberline in Yarlung Zangpo Grand Canyon Nature Reserve by using remote sensing data and spatial analysis based on land use/land cover classification and NDVI distribution characteristics.Combining DEM data,the influence of slope and aspect on the distribution of alpine timberline was explored.The results showed that the alpine timberline in Yarlung Zangpo Grand Canyon is transitional timberline,with the upper boundary approximately distributed at the elevation of 3422-4373 m,the lower boundary at approximately 3270-4164 m,with a width of about 110-280 m.Alpine timberline was mainly distributed on steep and very steep slopes ranging from 25°to 45°.The maximum elevation of both the upper and lower boundaries occurred on steep slopes.The distribution of alpine timberline varies with aspects,with sunny slopes having a higher boundary than shady slopes.

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

Classification of Remote Sensing Images Based on Band Selection and Multi-mode Feature Fusion

As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.

Research on Extraction Method of Surface Information Based on Multi-Feature Combination Such as Fractal Texture

Because of the developed economy and lush vegetation in southern China, the following obstacles or difficulties exist in remote sensing land surface classification: 1) Diverse surface composition types;2) Undulating terrains;3) Small fragmented land;4) Indistinguishable shadows of surface objects. It is our top priority to clarify how to use the concept of big data (Data mining technology) and various new technologies and methods to make complex surface remote sensing information extraction technology develop in the direction of automation, refinement and intelligence. In order to achieve the above research objectives, the paper takes the Gaofen-2 satellite data produced in China as the data source, and takes the complex surface remote sensing information extraction technology as the research object, and intelligently analyzes the remote sensing information of complex surface on the basis of completing the data collection and preprocessing. The specific extraction methods are as follows: 1) extraction research on fractal texture features of Brownian motion;2) extraction research on color features;3) extraction research on vegetation index;4) research on vectors and corresponding classification. In this paper, fractal texture features, color features, vegetation features and spectral features of remote sensing images are combined to form a combination feature vector, which improves the dimension of features, and the feature vector improves the difference of remote sensing features, and it is more conducive to the classification of remote sensing features, and thus it improves the classification accuracy of remote sensing images. It is suitable for remote sensing information extraction of complex surface in southern China. This method can be extended to complex surface area in the future.

How to accurately extract large-scale urban land?Establishment of an improved fully convolutional neural network model

Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.