Delineating suspended sediment concentration patterns in surface waters of the Changjiang Estuary by remote sensing analysis

Three Landsat TM imageries （taken on 18 May 1987,4 August 1998 and 28 July 2007） were used as the data source to identify the spatial and temporal variations of the suspended sediment concentration （SSC） in surface waters of the Changjiang Estuary.Atmospheric correction was carried out to determine the water-leaving reflectance using the FLAASH module.A regression equation between surveyed SSC and suspended sediment index was chosen to retrieve the SSC from the Landsat TM images.In addition,tidal harmonic analysis was performed to calculate tidal conditions corresponding to the acquisition time of satellite images.The results show that the SSC spatial patterns are similar to the in situ observation results,which show the highest SSC in the region of turbidity maximum zone in the Changjiang Estuary.For the period of 1987 to 2007,the SSC pattern is controlled mainly by tidal dynamic conditions and wind speeds,rather than sediment discharges from the river.

Weighted spectral correlation angle target detection method for land‑based hyperspectral imaging

In land-based spectral imaging,the spectra of ground objects are inevitably afected by the imaging conditions(weather conditions,atmospheric conditions,light conditions,zenith and azimuth angle conditions)and spatial distribution of targets,leading to uncertainties featured by“same object diferent spectrum”.That is,the spectrum of a ground object may change within a certain range under diferent imaging conditions.Traditional target detection(TD)methods are mainly based on similarity measurements and do not fully account for the spectral uncertainties.These detection methods are prone to false detections or missed detections.Therefore,reducing the impact of spectral uncertainties on TD is an important research topic in hyperspectral imaging.In this paper,we frst review traditional TD methods and compare their principles and characteristics.It is found that the spectral correlation angle(SCA)method has good adaptability in land-based imaging.The shortcoming of the SCA method that it cannot refect the local spectrum characteristics,is also analyzed.As the efect of spectral uncertainties cannot be completely overcome by the SCA method,a new similarity measurement method,the weighted spectral correlation angle(WSCA)method,is proposed.It can reduce the infuence of spectral uncertainties on TD by increasing the weight of particular bands.Finally,we use two sets of experiments to analyze the efect of the WSCA method on TD.Its performance in overcoming spectral uncertainties caused by variations in imaging conditions or uneven spatial distributions of targets is tested.The results show that the WSCA method can efectively reduce the infuence of spectral uncertainties and obtain a good detection result.

High fidelity virtual try-on network via semantic adaptation and distributed componentization

Image-based virtual try-on systems have significant commercial value in online garment shopping.However,prior methods fail to appropriately handle details,so are defective in maintaining the original appearance of organizational items including arms,the neck,and in-shop garments.We propose a novel high fidelity virtual try-on network to generate realistic results.Specifically,a distributed pipeline is used for simultaneous generation of organizational items.First,the in-shop garment is warped using thin plate splines(TPS)to give a coarse shape reference,and then a corresponding target semantic map is generated,which can adaptively respond to the distribution of different items triggered by different garments.Second,organizational items are componentized separately using our novel semantic map-based image adjustment network(SMIAN)to avoid interference between body parts.Finally,all components are integrated to generatethe overall result by SMIAN.A priori dual-modalinformation is incorporated in the tail layers of SMIAN to improve the convergence rate of the network.Experiments demonstrate that the proposed method can retain better details of condition information than current methods.Our method achieves convincing quantitative and qualitative results on existing benchmark datasets.