Special section editors Sicong Liu, Francesca Bovolo, Claudio Persello, Danfeng Hong, and Alim Samat introduce the Special Section on Advanced Spectral Analysis Techniques and Remote Sensing Applications.
Extraction of water body information from synthetic aperture radar (SAR) images plays a crucial role in urban flood monitoring. Traditional threshold segmentation methods are commonly employed in water body extraction due to the advantages of not requiring labeled samples and high computational efficiency. However, in complex urban terrains, the optimal threshold may be offset by data quality. To address this challenge, we introduce an adaptive iterative thresholding segmentation method guided by optical prior water body information. First, optical images captured before the disaster are used to identify inherent water body areas within the city. Second, the SAR data coverage areas corresponding to the inherent areas are taken as prior information on water bodies during the disaster. Finally, an adaptive iterative thresholding segmentation method based on prior water body information is constructed to automatically extract urban water bodies from SAR images. To validate the effectiveness of this approach, water body extraction experiments in urban inundation zones are conducted using Sentinel-1 and GF-3 SAR data in Beijing, Shijiazhuang, and Zhengzhou. The results show that the overall accuracy of the method used in this study is 79%, 95%, and 97.8% on Sentinel-1 images in Beijing, Shijiazhuang, and Zhengzhou, respectively, and 91.1% on GF-3 images in Zhengzhou. The experimental results in different regions are favorable and have a certain universality. Meanwhile, compared with traditional threshold segmentation methods, this method improves the accuracy of urban water extraction by at least 2% on Sentinel-1 and GF-3 SAR images, providing a more effective technical means for water extraction in urban flood inundation areas.
This paper presents a novel approach to the task of hyperspectral signature analysis. Hyperspectral signature analysis has been studied a lot in literature and there has been a lot of different algorithms developed which endeavors to discriminate between hyperspectral signatures. There are many approaches for performing the task of hyperspectral signature analysis. Binary coding approaches like SPAM and SFBC use basic statistical thresholding operations to binarize a signature which are then compared using Hamming distance. This framework has been extended to techniques like SDFC wherein a set of primate structures are used to characterize local variations in a signature together with the overall statistical measures like mean. As we see such structures harness only local variations and do not exploit any covariation of spectrally distinct parts of the signature. The approach of this research is to harvest such information by the use of a technique similar to circular convolution. In the approach we consider the signature as cyclic by appending the two ends of it. We then create two copies of the spectral signature. These three signatures can be placed next to each other like the rotating discs of a combination lock. We then find local structures at different circular shifts between the three cyclic spectral signatures. Texture features like in SDFC can be used to study the local structural variation for each circular shift. We can then create different measure by creating histogram from the shifts and thereafter using different techniques for information extraction from the histograms. Depending on the technique used different variant of the proposed algorithm are obtained. Experiments using the proposed technique show the viability of the proposed methods and their performances as compared to current binary signature coding techniques.
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