Abstract
Mangroves are valuable contributors to coastal ecosystems, and remote sensing is an indispensable way to obtain knowledge of the dynamics of mangrove ecosystems. Due to the similar spectral features between mangroves and other land cover types, challenges are posed since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this paper, we propose a classification method named the multi-feature joint sparse algorithm (MF-SRU), in which spectral, topographic, and textural features are integrated as the decision-making features, and sparse representation of both center pixels and their eight neighborhood pixels is proposed to represent the spatial correlation of neighboring pixels, which can make good use of the spatial correlation of adjacent pixels. Experiments are performed on Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in Southeastern China, and the results show that the proposed method can effectively improve the extraction accuracy of mangroves.
Highlights
Remote sensing technology has been widely used in various fields, such as military, agriculture, forestry, environmental monitoring, geology, and so on [1]
The computational complexity of the multi-feature joint sparse model algorithm (MF-SRU) method depends on the processing of dictionary training and the calculating of the sparse coefficient
The remote sensing data used in this experiments are derived from the LANDSAT 5 Thematic Mapper (TM) on 25 September 2006, which is the sampling of the Zhangjiangkou Mangrove Nature Reserve in Fujian province
Summary
Remote sensing technology has been widely used in various fields, such as military, agriculture, forestry, environmental monitoring, geology, and so on [1]. The detection and analysis of the distribution of mangroves are important for their protection. Due to their special growth environment, remote sensing technology gives people a new way to map and monitor the dynamic change of mangroves. Classification is the core issue in the application of mangrove remote sensing images. There are two main methods: supervised and unsupervised [4]. Remote sensing image classification methods are mainly based on supervised methods, such as support vector machine (SVM) [5], maximum likelihood method (ML) [6], and so on. A number of algorithms have been proposed for classification, such as the object-based remote sensing image analysis approach [7,8]
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