Convolutional neural networks (CNNs) have significance in remote sensing image mapping, and pixel-level representation allows refined results. Due to inconsistencies within a class and different scales of water bodies, the water body mapping has challenges, such as insufficient integrity and rough shape segmentation. To resolve these issues, we proposed an intelligent water bodies extraction method (named SADA-Net) for high-resolution remote sensing images. This method considers multiscale information, context dependence, and shape features. The network framework integrates three critical components: shape feature optimization (SFO), atrous spatial pyramid pooling, and dual attention modules. SADA-Net can accurately extract an extensive range of water bodies in complex scenarios. SADA-Net has certain advantages regarding small and dense water bodies extraction, as the SFO module effectively solves the defects of the unified processing of low-level features in the encoder stage of CNNs, which highlights the shape information of a water body. Two data types (red, green, and blue bands and multispectral images) are employed to verify the performance of the proposed network. The best result achieved an evaluation index F1-Score of 96.14% in large-scale image segmentation, and the structural similarity index measure reached 94.70%. Overall, the proposed method achieves the purpose of maximizing the integrity and optimizing the shape of a water body. Additionally, the SADA-Net proposed in this article has a specific reference value for high-resolution remote sensing image water bodies mapping.

Water body mapping is significant for water resource management. In the view of 21 spectral bands and a short revisit time of no more than two days, a Sentinel-3 OLCI (Ocean and Land Colour Instrument) image could be the optimum data source in the near-real-time mapping of water bodies. However, the image is often limited by its low spatial resolution in practice. Super-resolution mapping (SRM) is a good solution to generate finer spatial resolution maps than the input data allows. In this paper, a multiscale spatiotemporal super-resolution mapping (MSST_SRM) method for water bodies is proposed, particularly for Sentinel-3 OLCI images. The proposed MSST_SRM method employs the integrated Normalized Difference Water Index (NDWI) images calculated from four near-infrared (NIR) bands and Green Band 6 of the Sentinel-3 OLCI image as input data and combined the spectral, multispatial, and temporal terms into one objective function to generate a fine water body map. Two experiments in the Tibet Plate and Daye lakes were employed to test the effectiveness of the MSST_SRM method. Results revealed that by using multiscale spatial dependence under the framework of spatiotemporal super-resolution Mapping, MSST_SRM could generate finer water body maps than the hard classification method and the other three SRM-based methods. Therefore, the proposed MSST_SRM method shows marked efficiency and potential in water body mapping using Sentinel-3 OLCI images.

Water is an important element in the ecological environment, and different types of water (e.g., rivers, lakes, and ponds) have different impacts on the ecology. The extraction and classification of different types of water bodies has significant implications for the water resource management and water environment monitoring. Current research on the water-body types classification is relatively limited compared to water body extraction. Existing methods typically adopt a two-stage architecture, where the first stage extracts water bodies at the pixel level, and the second stage classifies the water bodies into different types using rule-based thresholds classifier and morphological features at object level. However, methods in the second stage suffer from overfitting, lack of robustness, and confusion in object segmentation. Despite these challenges, the deep learning methods could capture the high-level semantic features, which are effective for the classification of different types of water bodies. In this paper, a novel Water-Scene Enhancement Deep Model (WSEDM) was proposed for identifying multiple types of water bodies. The WSEDM consists of a pixel-wise water body extraction using Edge-Otsu and a patch-wise water-body types classification through deep learning model. In order to improve the accuracy of patch-wise water body classification, a novel multimodal feature fusion network(CASANet) was designed for the fusing of optical and SAR images. The water-body types classification was conducted on three international wetland cities in the urban agglomeration in the middle reaches of the Yangtze River. The 10m water-body types map achieved an overall accuracy of 94.6%. The proposed CASANet is also validated through comparison and transferability experiments, which further confirmed the superior performance.

Water body mapping is an effective way to monitor dynamic changes in surface water, which is of great significance for water resource management. Super-resolution mapping is a valid method to generate high-resolution dynamic water body maps from low-spatial-resolution images. However, the accuracy of existing super-resolution mapping methods is not high due to the low accuracy of fraction images and the insufficiency of spatial pattern information. To solve this problem, this paper proposes a spectral similarity scale-based multiple-endmember spectral mixture analysis (SSS-based MESMA) and a multiscale spatio-temporal dependence method based on super-resolution mapping (MESMA_MST_SRM) for water bodies. SSS-based MESMA allows different coarse pixels to have different endmember combinations, which can effectively improve the accuracy of spectral unmixing and then improve the accuracy of fraction images. Multiscale spatio-temporal dependence adopts both pixel-based and subpixel-based spatial dependence. In this study, eight different types of water body mappings derived from the Landsat 8 Operational Land Imager (OLI) and Google Earth images were employed to test the performance of the MESMA_MST_SRM method. The results of the eight experiments showed that compared with the other four tested methods, the overall accuracy (OA) value, as well as the overall distribution and detailed information of the water map generated by the MESMA_MST_SRM method, were the best, indicating the great potential and efficiency of the proposed method in water body mapping.

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.