Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection

Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 5.82%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy.

Accurate building extraction from very high-resolution (VHR) remote sensing images plays an important role in urban dynamic monitoring, planning, and management. However, it is still a challenging task to achieve building extraction with high accuracy and integrity due to diverse building appearances and more complex ground background in VHR remote sensing images. Recently, Unity Networking (UNet) has been proven to be capable of feature extraction and semantic segmentation of remote sensing images. However, UNet cannot achieve sufficient multi-scale and multi-level features with larger receptive fields. To address these problems, an improved network based on UNet structure (Refine-UNet) is proposed for extracting buildings from the VHR images. The proposed Refine-UNet mainly consists of encoder module, decoder module, and refine skip connection scheme. The refine skip connection scheme is composed of an atrous spatial convolutional pyramid pooling (ASPP) module and several improved depthwise separable convolution (IDSC) modules. Experimental results on the Jilin-1 VHR datasets with a spatial resolution of 0.75 m demonstrate that compared with UNet, PSPNet, DeepLabV3+, and SegNet, the proposed Refine-UNet can obtain more accurate building extraction results and achieve the best Precision of 95.1% and intersection over union (IoU) of 87.0%, respectively, indicating the great practical potential.

This paper presents a hierarchical image segmentation method that combines multichannel watershed transformation and dynamics of watershed contours for the segmentation of very high resolution (VHR) multispectral imagery. The image gradient was first extracted from a multispectral image using a multichannel morphological method, followed by classical watershed transformation to produce an initial segmentation result. The resulting watershed contours were then analyzed according to their relevance relative to the minima of the adjacent basins to construct an image containing information about their dynamics. By thresholding the image of the contour dynamics at different levels, multilevel hierarchical segmentation results with different levels of detail were achieved. The proposed method was evaluated by comparing with existing methods through visual inspection, quantitative measures and applications in urban impervious surface mapping, using two sets of VHR image data. The experimental results showed that the proposed method produced more accurate segmentation results compared to an existing single-level segmentation method, in terms of visual and quantitative evaluations. While used for urban impervious surface mapping, the proposed method achieved an overall accuracy significantly higher than the pixel based classification method, and also higher than the object based classification using a single-level segmentation result. Compared with the most widely used segmentation method implemented in the eCognition, the proposed method achieved a comparable performance, although they have different segmentation details. The proposed segmentation method can be used in relevant VHR image processing and applications.

Accurate and reliable farmland crop mapping is an important foundation for relevant departments to carry out agricultural management, crop planting structure adjustment and ecological assessment. The current crop identification work mainly focuses on conventional crops, and there are few studies on parcel-level mapping of horticultural crops in complex mountainous areas. Using Miaohou Town, China, as the research area, we developed a parcel-level method for the precise mapping of horticultural crops in complex mountainous areas using very-high-resolution (VHR) optical images and Sentinel-2 optical time-series images. First, based on the VHR images with a spatial resolution of 0.55 m, the complex mountainous areas were divided into subregions with their own independent characteristics according to a zoning and hierarchical strategy. The parcels in the different study areas were then divided into plain, greenhouse, slope and terrace parcels according to their corresponding parcel characteristics. The edge-based model RCF and texture-based model DABNet were subsequently used to extract the parcels according to the characteristics of different regions. Then, Sentinel-2 images were used to construct the time-series characteristics of different crops, and an LSTM algorithm was used to classify crop types. We then designed a parcel filling strategy to determine the categories of parcels based on the classification results of the time-series data, and accurate parcel-level mapping of a horticultural crop orchard in a complex mountainous area was finally achieved. Based on visual inspection, this method appears to effectively extract farmland parcels from VHR images of complex mountainous areas. The classification accuracy reached 93.01%, and the Kappa coefficient was 0.9015. This method thus serves as a methodological reference for parcel-level horticultural crop mapping and can be applied to the development of local precision agriculture.

The paper focuses on the application of the Cross-Correlation Analysis (CCA) technique for quantifying changes of semi-natural grasslands to artificial structures at different spatial resolutions (grain) on a Mediterranean Natura 2000 site as a further test case of the results reported in [1]. In that work the CCA was applied to detect specific changes associated with agricultural intensification and fires. A semi-natural grasslands layer extracted from an existing Land Cover/Land Use map (1:5000, time T1) was considered as input to the CCA jointly with a Very High Resolution (VHR) WorldView-2 satellite image (2 meters spatial resolution, time T2) and with a High Resolution Landsat 8 OLS satellite image (30 meters spatial resolution, time T2), with T2 > T1, respectively, for the fine and the coarse scale analysis. The results were compared to those obtained by applying a traditional Post Classification Comparison technique to the same reference at time T1 map and an updated at time T2 map obtained by a knowledge driven classification of four multi-seasonal Worldview-2 input images, according to [1]. Also in this case the CCA technique results encouraging offering the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 is too expensive or when no archive VHR image is available in the past for comparison between at time T1 and T2 images. The areas of change detected at VHR and HR were quite similar for the specific transition analyzed with larger error values in HR change images.

In this article, we develop a novel method for the detection of vineyard parcels in agricultural landscapes based on very high resolution (VHR) optical remote sensing images. Our objective is to perform texture-based image retrieval and supervised classification algorithms. To do that, the local textural and structural features inside each image are taken into account to measure its similarity to other images. In fact, VHR images usually involve a variety of local textures and structures that may verify a weak stationarity hypothesis. Hence, an approach only based on characteristic points, not on all pixels of the image, is supposed to be relevant. This work proposes to construct the local extrema-based descriptor (LED) by using the local maximum and local minimum pixels extracted from the image. The LED descriptor is formed based on the radiometric, geometric and gradient features from these local extrema. We first exploit the proposed LED descriptor for the retrieval task to evaluate its performance on texture discrimination. Then, it is embedded into a supervised classification framework to detect vine parcels using VHR satellite images. Experiments performed on VHR panchromatic PLEIADES image data prove the effectiveness of the proposed strategy. Compared to state-of-the-art methods, an enhancement of about 7% in retrieval rate is achieved. For the detection task, about 90% of vineyards are correctly detected.

Abstract. Very high resolution (VHR) remote sensing imagery can reveal the ground object in greater detail, depicting their color, shape, size and structure. However, VHR also leads much original noise in spectra, and this original noise may reduce the reliability of the classification’s result. This paper presents an Adaptive Morphological Mean Filter (AMMF) for smoothing the original noise of VHR imagery and improving the classification’s performance. AMMF is a shape-adaptive filter which is constructed by detecting gradually the spectral similarity between a kernel-anchored pixel and its contextual pixels through an extension-detector with 8-neighbouring pixels, and the spectral value of the kernel-anchored pixel is instead by the mean of group pixels within the adaptive region. The classification maps based on the AMMF are compared with the classification of VHR images based on the homologous filter processing, such as Mean Filter (MF) and Median Filter(MedF). The experimental results suggest the following: 1) VHR image processed using AMMF can not only preserve the detail information among inter-classes but also smooth the noise within intra-class; 2) The proposed AMMF processing can improve the classification’s performance of VHR image, and it obtains a better visual performance and accuracy while comparing with MF and MedF.

This paper focuses on the application of a semi-automatic unsupervised change detection algorithm called Cross Correlation Analysis (CCA) to the detection of (semi-) natural grasslands changes at Very High Resolution (VHR). A reference validated Land Cover/Land Use map at time T1 and only one satellite image at time T2, with T2>T1, are required to detect changes occurred at T2 in the selected target class. This approach offers the possibility to reduce the costs of change detection when the acquisition of multi-seasonal VHR images at time T2 for supervised change detection is too expensive or when no archive VHR image is available in the past for unsupervised comparison between T1 and T2 images. A summer Worldview-2 image for a Natura 2000 test site was considered and the results appear encouraging.

Building detection in very high resolution (VHR) remote sensing images is crucial for many urban planning and management applications. Since buildings are elevated objects, the incorporation of elevation data provides a mean to reliable detection. However, almost all existing methods of elevation-based building detection must first generate a normalized Digital Surface Model (nDSM). This model is generated by processes of extracting and subtracting terrain elevations from the DSM data. The generation of accurate nDSM is still a challenging task to some extent. This paper introduces a segment-based terrain filtering (SegTF) technique to filter out the terrain elevations directly using DSM elevations. This technique has four steps: elevation co-registration, image segmentation, slope calculation, and building detection. These steps of the developed technique were applied to a dataset that consisted of a VHR image and a corresponding DSM for detecting buildings. The result of the building detection was evaluated and found to be 100% correct with an overall detection quality of 93%. These values indicate a highly reliable and promising technique for mapping buildings in VHR images.

Multi-temporal remote sensing dataset allows for cost-and time-efficient monitoring of land-cover vital to the conservation of ecosystems and biodiversity. The Very High Resolution (VHR) images provide the needed spatial resolution which is important for government management and decision-making. In this paper, we propose a workflow for detecting artificial surface expansion in ecological redline zones based on object-based image analysis and transfer matrix. The new increased artificial surface regions are further obtained by rule optimization in the preliminary results. Finally, 854 new artificial surface patches with a total area of more than 1.93 square kilometers were detected. The proposed approach quantifies the effects of human activities without high cost and time-consuming process, which is of great significance for the protection and management of ecological redline zones.

Satellite imagery has been used to improve scientific research worldwide. In this study, the southern right whale (Eubalaena australis) was chosen to test the use of medium, high, and very high resolution (VHR) satellite images, on the Brazilian breeding ground. These images were used to identify the whales and were compared to aerial survey data collected in the same area. The VHR satellite images from the Pleiades‐1A satellite, available on Google Earth, displayed the best results when compared to those from Sentinel 2, Landsat 8, Rapid Eye, and Planet Scope. No significant differences were observed (Mann‐Whitney U test) between the possible whales recorded in the satellite images and of real whales recorded in situ by aerial surveys, while considering either the number of groups (p = .841, n = 5) or the total number of animals (p = .222, n = 5). Further, when using VHR images, the geographical positions of the whales recorded in situ had a positive correlation with the positions generated from the satellite images (Mantel test: r = 0.52, p = .001, n = 13). This technique may represent an important tool for detecting right whales, especially in countries where research funding is scarce.

Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets.

In the past few decades, China's rapid industrial activities and urbanization processes have greatly impacted the urban surface water ecosystem. The changes in the quality of urban surface water directly affect the supply and carbon cycling of urban waters. We collected 50 water samples from urban rivers, lakes, and reservoirs in the city of Changchun in June 2020. Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis (EEMs-PARAFAC) was used to unravel the optical characteristics, composition, and sources of chromophoric dissolved organic matter (CDOM). Our results indicated that the mean concentration of DOC is significantly higher in urban rivers than in reservoirs (t-test, P<0.05), and the mean UV absorption coefficient of CDOM a254 of urban rivers is significantly larger than that of park lakes and reservoirs (t-test, P<0.05), indicating that urban rivers have the highest concentration of CDOM. The spectral slope of CDOM absorption S275-295 and the spectral slope ratio SR were shown to be higher in park lakes than in reservoirs, and even higher than in urban rivers (t-test, P<0.001). Three fluorescent components were obtained using PARAFAC, namely terrestrial human-like (C1), microbial human-like (C2) and tryptophan-like (C3) components. The mean fluorescence intensity of C1-C3 was significantly higher in urban rivers than in both the park lakes and reservoirs (t-test, P<0.005), and the mean fluorescence intensity of C1 in the reservoir water body was significantly higher than that of C2 and C3 (t-test, P<0.005), indicating that the discharge of municipal wastewater likely contributes significantly to the CDOM pool of urban rivers in Changchun, and the contribution percentages of highly bio-labile protein-like components to the CDOM pool in these waters are high. Urban wastewater treatment should be strengthened to effectively protect water quality, as well as the economic, environmental, and ecological functions of urban waters in Changchun City.

Convolutional Neural Network (CNN) is widely used for semantic segmentation and land-use and land-cover (LULC) mapping of very high-resolution (VHR) remote sensing images. The convolution operation is a powerful method for VHR classification, but the loss of high-frequency detail information caused during its operation decreases the classification accuracy, particularly in the boundary. Thus, it is necessary to supply additional boundary information to the CNN for alleviating this situation. In the classification task (and in LULC mapping), providing more effective information generates a better classification result. Current methods regard the boundary of images as the same category object and process it uniformly, which loses a notable amount of useful information because of the different properties, such as ambiguity and transition, between remote sensing images and their boundaries. Thus, a semantic segmentation method with category boundary for LULC mapping is proposed in this paper. First, a multi-task CNN called the category boundary detection network (CBDN) is designed to extract the boundary information of different category objects. Second, this category boundary and VHR images are used for initial semantic segmentation. Finally, the category boundary and the initial semantic segmentation result (ISSR) are fused to obtain the final LULC map by a two-step strategy, including the explicit fusion and the boundary attention loss function. To verify whether category boundary improved the classification accuracy, a set of comparative experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets. The method in this paper was compared with a semantic segmentation method with no boundary information and a semantic segmentation method with global boundary. The results showed that the proposed method in this paper achieved good performances in the Vaihingen (overall accuracy (OA) = 0.924, Kappa coefficient (K) = 0.898, mean F1 score (mF1) = 0.896 and mean Intersection over Union (mIoU) = 0.817) and Potsdam datasets (OA = 0.890, K = 0.857, mF1 = 0.923, and mIoU = 0.860) based on the eroded labels.