Abstract
Recent studies have shown that deep learning methods provide useful tools for wetland classification. However, it is difficult to perform species-level classification with limited labeled samples. In this paper, we propose a semi-supervised method for wetland species classification by using a new efficient generative adversarial network (GAN) and Jilin-1 satellite image. The main contributions of this paper are twofold. First, the proposed method, namely ShuffleGAN, requires only a small number of labeled samples. ShuffleGAN is composed of two neural networks (i.e., generator and discriminator), which perform an adversarial game in the training phase and ShuffleNet units are added in both generator and discriminator to obtain speed-accuracy tradeoff. Second, ShuffleGAN can perform species-level wetland classification. In addition to distinguishing the wetland areas from non-wetlands, different tree species located in the wetland are also identified, thus providing a more detailed distribution of the wetland land-covers. Experiments are conducted on the Haizhu Lake wetland data acquired by the Jilin-1 satellite. Compared with existing GAN, the improvement in overall accuracy (OA) of the proposed ShuffleGAN is more than 2%. This work can not only deepen the application of deep learning in wetland classification but also promote the study of fine classification of wetland land-covers.
Highlights
Wetlands [1], known as “the kidney of the earth,” are highly productive and biodiverse ecosystems that play vital roles in environmental sustainability, and mapping wetlands is crucially important for us to survey the species distribution and analyze the dynamic changes of the wetland area [2,3]
The superpixel segmentation results of the Haizhu Lake wetland data are shown in Figure 8, it is found that the experimental data is flexibly divided into naturally formed spatial areas with irregular sizes and shapes
It can be first seen that the support vector machine (SVM) and Laplacian SVM (LapSVM), which are based only on spectral information, achieve lower classification performance than the results obtained by spectral-spatial features
Summary
Wetlands [1], known as “the kidney of the earth,” are highly productive and biodiverse ecosystems that play vital roles in environmental sustainability, and mapping wetlands is crucially important for us to survey the species distribution and analyze the dynamic changes of the wetland area [2,3]. Remote sensing technologies [4] have proven to be effective tools in mapping the wetland distribution. One of the major imagery sources is multispectral data, including Landsat [5,6], Sentinel [7,8] and WorldView [9,10]. Wetland classification applications are advancing, many moving from low and moderate resolution imagery to high resolution
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
