New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

Abstract

Abstract. Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Water Bodies’ Mapping from Sentinel-2 Imagery with Modified Normalized Difference Water Index at 10-m Spatial Resolution Produced by Sharpening the SWIR Band

“…At present, remote sensing has become a routine approach for land surface water bodies' monitoring, because the acquired data can provide macroscopic, real-time, dynamic and cost-effective information, which is substantially different from conventional in situ measurements [4][5][6]. Various methods, including single band density slicing [7], unsupervised and supervised classification [8][9][10][11] and spectral water indexes [12][13][14][15][16][17][18][19], were developed in order to extract water bodies from different remote sensing images. Among all existing water body mapping methods, the spectral water index-based method is a type of reliable method, because it is user friendly, efficient and has low computational cost [20].…”

A Natural-Rule-Based-Connection (NRBC) Method for River Network Extraction from High-Resolution Imagery

“…Water body detection methods include thresholding of water indices, edge detection and region growth, classification methods, and their combinations [12][13][14][15]. Popular water indexes include normalized difference water index (NDWI) [16], the modified NDWI (MNDWI) [17], water index (WI), land surface water index (LSWI), automated water extraction index (AWEI) [9,18,19], and indexes based on image texture and local entropy [20]. Thresholding of water indexes is an intuitive and concise approach (e.g., [21]) but the resulting water masks tend to include a lot of misclassified regions.…”

“…There have been many methods developed for water body extraction (e.g., [4][5][6][7]) and method comparison [8][9][10][11]. Water body detection methods include thresholding of water indices, edge detection and region growth, classification methods, and their combinations [12][13][14][15].…”

Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery

“…In the experiments, the Beijing test area resulted in the lowest water mapping accuracy, with a kappa coefficient of 0.7392, followed by the Shanghai test area, with a kappa coefficient of 0.8994. Through a comprehensive analysis of the error statistics, we believe that the error source comes from three aspects: (1) all four test images were acquired in urban areas with complex ground surface conditions, especially for the Beijing and Shanghai test areas, which include many dark buildings and building shadows, which are easily confused with water bodies due to the high similarity in the spectral features [15]; (2) the technique of the selection of the most representative endmembers, accounting for land surface similarity in an adjacent space, is proposed. However, this may not agree with the spectra of the fractions in the mixed pixels, and could result in large residuals in the unmixing process; and (3) the linear unmixing method was applied for the water abundance estimation in the study; however, the measured signal of the sensor always results from the interactions of electromagnetic radiation with the multiple constituents within each pixel [52].…”