D-SS Frame:deep spectral-spatial feature extraction and fusion for classification of panchromatic and multispectral images

Facing the very high-resolution( VHR) image classification problem,a feature extraction and fusion framework is presented for VHR panchromatic and multispectral image classification based on deep learning techniques. The proposed approach combines spectral and spatial information based on the fusion of features extracted from panchromatic( PAN) and multispectral( MS) images using sparse autoencoder and its deep version. There are three steps in the proposed method,the first one is to extract spatial information of PAN image,and the second one is to describe spectral information of MS image. Finally,in the third step,the features obtained from PAN and MS images are concatenated directly as a simple fusion feature. The classification is performed using the support vector machine( SVM) and the experiments carried out on two datasets with very high spatial resolution. MS and PAN images from WorldView-2 satellite indicate that the classifier provides an efficient solution and demonstrate that the fusion of the features extracted by deep learning techniques from PAN and MS images performs better than that when these techniques are used separately. In addition,this framework shows that deep learning models can extract and fuse spatial and spectral information greatly,and have huge potential to achieve higher accuracy for classification of multispectral and panchromatic images.

Fusion of multispectral image and panchromatic image based on NSCT and NMF

A novel fusion method of multispectral image and panchromatic image based on nonsubsampled contourlet transform（NSCT） and non-negative matrix factorization（NMF） is presented,the aim of which is to preserve both spectral and spatial information simultaneously in fused image.NMF is a matrix factorization method,which can extract the local feature by choosing suitable dimension of the feature subspace.Firstly the multispectral image was represented in intensity hue saturation（IHS） system.Then the I component and panchromatic image were decomposed by NSCT.Next we used NMF to learn the feature of both multispectral and panchromatic images＇ low-frequency subbands,and the selection principle of the other coefficients was absolute maximum criterion.Finally the new coefficients were reconstructed to get the fused image.Experiments are carried out and the results are compared with some other methods,which show that the new method performs better in improving the spatial resolution and preserving the feature information than the other existing relative methods.

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies,tree stem structure variables(SSVs)proved to be an essential kind of parameters,and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle.For this newly emerging task,satellite imagery such as WorldView-2 panchromatic images(WPIs)is used as a potential solution for co-prediction of tree-level multifarious SSVs,with static terrestrial laser scanning(TLS)assumed as a‘bridge’.The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters,and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models(termed as Model1s and Model2s).In the case of Picea abies,Pinus sylvestris,Populus tremul and Quercus robur in a boreal forest,tests showed that Model1s and Model2s for different tree species can be derived(e.g.the maximum R^(2)=0.574 for Q.robur).Overall,this study basically validated the algorithm proposed for co-prediction of multifarious SSVs,and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling,which is useful for large-scale investigations of forest understory,macroecosystem ecology,global vegetation dynamics and global carbon cycle.

MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

Remote Sensing image fusion is an effective way to use the large volume ofdata from multi-source images. This paper introduces a new method of remote sensing image fusionbased on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectralremote sensing data SPOT-4. Firstly, the new method is established by building a model of remotesensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classificationfusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) Fromsubjectivity assessment, the spatial resolution of the fused image is improved compared to theSPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitativeanalysis, the effect of classification fusion is better. As a whole, the re-suit shows that theaccuracy of image fusion based on SVM is high and the SVM algorithm can be recommended forapplication in remote sensing image fusion processes.

Assessment and Evaluation of Band Ratios, Brovey and HSV Techniques for Lithologic Discrimination and Mapping Using Landsat ETM⁺and SPOT-5 Data

This study aims to assess and to evaluate band ratios, brovey and HSV (Hue-Saturation-Value) techniques for discrimination and mapping the basement rock units exposed at Wadi Bulghah area, Saudi Arabia using multispectral Landsat ETM+ and SPOT-5 panchromatic data.?FieldSpec instrument is utilized to collect the spectral data of diorite, marble, gossan and volcanics, the main rock units exposed at the study area. Spectral profile of diorite exhibits very distinguished absorption features around 2.20 μm and 2.35 μm wavelength regions. These absorption features lead to lowering the band ratio values within the band-7 wavelength region. Diorite intrusions appear to have grey and dark grey image signatures on 7/3 and 7/2 band ratio images respectively. On the false color composite ratio image (7/3:R;7/2:G and 5/2:B), diorite, marble, gossan and volcanics have very dark brown, dark blue, white and yellowish brown image signatures respectively. Image fusion between previously mentioned FCC ratio image and high spatial resolution (5 meters) SPOT-5 panchromatic image is carried out by using brovey and HSV transformation methods. Visual and statistical assessment methods prove that HSV fused image yields best image interpretability results rather than brovey image. It improves the spatial resolution of the original FCC ratios image with acceptable spectral preservation.

Joint AIHS and Particle Swarm Optimization for Pan-sharpening

Pan-sharpening is a process of obtaining a high spatial and spectral multispectral image(HMS)by combining a low-resolution multispectral image(LMS)with a high-resolution panchromatic image(PAN).In this paper,a pan-sharpening method called PAIHS is proposed,which is based on adaptive intensity-hue-saturation(AIHS)transformation,variational pan-sharpening framework and the two fidelity hypotheses.The suitable objective function is established and optimized by adopting particle swarm optimization(PSO)to obtain the optimal control parameters and minimum value.This value corresponds to the best pan-sharpening quality.The experimental results show that the proposed method has high efficiency and reliability,and the obtained performance index is superior to the four mainstream pan-sharpening methods.

Structural engineering of BaWO_(4)/CsPbX_(3)/CsPb_(2)X_(5)(X=Cl,Br,I)heterostructures towards ultrastable and tunable photoluminescence

Construction of lead halide perovskite nanocrystals(LHP NCs)heterostructures is essential to obtain highly stable photoluminescence and expand their applications.Herein,a novel self-assembly strategy combining with a solvent-free thermal-assisted synthesis and a water-triggered reaction is developed to subsequently grow BaWO_(4)/CsPbX_(3)/CsPb_(2)X_(5)(X=Cl,Br,I)heterostructures at low nucleation temperature with high crystallinity.The as-obtained ternary BaWO_(4)/CsPbX_(3)/CsPb_(2)X_(5)(X=Cl,Br,I)heterostructures exhibit remarkably enhanced panchromatic emission and ultrastable luminescence ascribing to the low-defect growth based on lattice matching.Stable white light-emitting diodes(WLEDs)have been constructed with a high correlated color temperature(CCT)of 7225 K and luminous efficiency of 74.4 lm·W-1.Ln^(3+)-doped BaWO_(4)/CsPbX_(3)/CsPb_(2)X_(5)(Ln^(3+)=Eu^(3+),Tb^(3+),Dy^(3+),Sm^(3+),Yb^(3+)/Er^(3+))nanocomposites are further designed with excitation-dependent photoluminescence and thermochromic properties,making them excellent candidates for high-level anti-counterfeiting and encryption.This work offers a green and universal approach in assembling CsPbX_(3)(X=Cl,Br,I)on lattice-matched tungstate with adjustable panchromatic emission for versatile optical applications.

Recent Progress on Organic Near-Infrared Photodetectors:Mechanism,Devices,and Applications

Comprehensive Summary Near infrared light organic photodetectors have attracted tremendous attention due to their tailorable response,ease of processing,compatibility with flexible substrate,room temperature operation and broad applications such as remote sensing,health monitoring,artificial vision,night vision,and so on.Recently,the great improvement obtained on the important figures of merit performances has made organic photodetectors catch up and even surpass those of inorganic photodetectors in some respects.In this review,after a brief illustration of the organic photodetectors'figures of merit performances,we summarize the research progress of panchromatic and narrowband near infrared light organic photodetectors from their working mechanism,strategies to achieve narrowband near infrared light organic photodetectors,to some practical applications.Finally,we discuss the development challenge of the near infrared light organic photodetectors.

Multi-spectral image fusion method based on two channels non-separable wavelets

A construction method of two channels non-separable wavelets filter bank which dilation matrix is [1, 1; 1,-1] and its application in the fusion of multi-spectral image are presented. Many 4×4 filter banks are designed. The multi-spectral image fusion algorithm based on this kind of wavelet is proposed. Using this filter bank, multi-resolution wavelet decomposition of the intensity of multi-spectral image and panchromatic image is performed, and the two low-frequency components of the intensity and the panchromatic image are merged by using a tradeoff parameter. The experiment results show that this method is good in the preservation of spectral quality and high spatial resolution information. Its performance in preserving spectral quality and high spatial information is better than the fusion method based on DWFT and IHS. When the parameter t is closed to 1, the fused image can obtain rich spectral information from the original MS image. The amount of computation reduced to only half of the fusion method based on four channels wavelet transform.

A pan-sharpening method based on the ADMM algorithm

Pan-sharpening is a method of integrating low-resolution multispectral images with corresponding high-resolution panchromatic images to obtain multispectral images with high spectral and spatial resolution. A novel variational model for pan-sharpening is proposed in this paper. The model is mainly based on three hypotheses: 1) the pan-shaipened image can be linearly represented by the corresponding panchromatic image;2) the low-resolution multispectral image is down-sampled from the high-resolution multispectral image through the down-sampling operator;and 3) the satellite image has the low-rank property. Three energy components corresponding to these assumptions are integrated into a variational framework to obtain a total energy function. We adopt the alternating direction method of multipliers (ADMM) to optimize the total energy function. The experimental results show that the proposed method performs better than other mainstream methods in spectral and spatial information preserving aspect.

Fusion of Remote Sensing Images Based on Nonsubsampled Contourlet Transform and Region Segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform(NSCT) and region segmentation.Firstly,the multispectral image is transformed to intensity-hue-saturation(IHS) system.Secondly,the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT.Then the NSCT coefficients of high and low frequency subbands are fused by different rules,respectively.For the high frequency subbands,the fusion rules are also unalike in the smooth and edge regions.The two regions are segregated in the panchromatic image,and the segmentation is based on particle swarm optimization.Finally,the fusion image can be obtained by performing inverse NSCT and inverse IHS transform.The experimental results are evaluated by both subjective and objective criteria.It is shown that the proposed method can obtain superior results to others.