Wetland Classification Based on a New Efficient Generative Adversarial Network and Jilin-1 Satellite Image

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.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

“…GAN generates new data instances that resemble the existing training data by the competition between a generator and a discriminator [267]. Several examples show the advantages of incorporating GAN models in hydrological classification [267][268][269] or combining it with autoencoder [270]. Integrating GAN with an LSTM network model [271][272][273]; combining GAN with an ANN fuzzy model [274] was also found to improve the automated hydrological and weather prediction using satellite data.…”

Nghiên cứu ứng dụng mô hình học máy trong phân loại hệ sinh thái đất ngập nước nội địa tại Vườn Quốc gia Ba Bể, tỉnh Bắc Kạn

“…Hai giai đoạn này được kết nối liên tục và đa chiều, giúp lưu giữ thông tin không gian và thuộc tính đến cùng. Đồng thời để quan sát các loại đất ngập nước trong một khu vực rộng lớn, các ảnh vệ tinh như MODIS, Landsat và Sentinel thường được sử dụng [5][6]. So với các hình ảnh vệ tinh MODIS và Landsat có độ phân giải không gian kém thì Sentinel với khả năng chụp ảnh đa phổ, có thể thu được hình ảnh quang học một cách có hệ thống trên cả khu vực nội địa và ven biển ở độ phân giải không gian cao (10 m đến 60 m).…”

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

“…There are two main ways to classify wetlands, which are landscape-and hierarchy-based classifications [26,28,53]. A hierarchical classification system (in which the attributes used to distinguish between levels with greater differences) is superior because it allows the classification according to different levels of detail.…”

“…Compared to the wetland classification systems of RAMSAR and MONRE, this study focuses on nine coastal wetland ecosystems in the dynamic estuary in the northeastern part of Vietnam (Figure 8). Although the wetland classification models were developed in some former studies [26,40,41,43,53], the classification models for the inland and coastal wetland ecosystem should be separated to provide suitable tools for different land managers. Most of the former studies only focused on the method or models to identify wetland in technical ways instead of on explaining how their outcomes have met the standard wetland classification systems and how to practically apply the trained models for land management [40,44].…”