HyperSpectral (HS) imaging is a significant tool in remote sensing applications. HS sensors measure the reflected light from the surface of objects in hundreds or thousands of Spectral bands، called HS images. Increasing the number of these bands produces huge data، which have to be transmitted to a terrestrial station for further processing. In some applications، HS images have to be sent instantly to the station requiring a high bandwidth between the sensors and the station. Most of the time، the bandwidth between the satellite and the station is narrowed limiting the amount of data that can be transmitted، and brings the idea of Compressive Sensing (CS) into the minds. In addition to the large amount of data، in these images، mixed pixels are another issue to be considered. Despite of their high Spectral resolution، their spatial resolution is low causing a mixture of spectra in each pixel، but not a pure spectrum. As a result، the analysis of mixed pixels or Spectral unmixing (SU) technique has been introduced to decompose mixed pixels into a set of endmembers and abundance fraction maps. The endmembers are extracted from Spectral signatures related to different materials، and the abundance fractions are the proportions of the endmembers in each pixel. In recent years، due to the large amount of data and consequently the difficulties of real-time signal processing، and also having the ability of image compression، methods of Compressive Sensing and unmixing (CSU) have been introduced. Two assumptions have been considered in these methods: the finite number of elements in each pixel and the low variation of abundance fractions. HYCA algorithm is one of the methods trying to compress these kinds of data with their inherent features. One of the sensible characteristics of this algorithm is to utilize spatial information for better reconstruction of the data. In fact، HYCA algorithm splits the data cube into non-overlapping square windows and assumes that Spectral vectors are similar inside each window. In this study، a real-time method is proposed، which uses the Spectral information (non-neighborhood pixels) in addition to the spatial information. The proposed structure can be divided into two parts: transmitting information into the satellites and information recovery into the stations. In the satellites، firstly، to utilize the Spectral information، a new real-time clustering method is proposed، wherein the similarity between the entire pixels is not restricted to any specific form such as square window. Figure 3 shows a segmented real HS image. It can be seen that the considering square form limits the capability of the HYCA algorithm and the similarity can be found in the both neighborhood and non-neighborhood pixels. Secondly، to utilize similarity in each cluster، different measurement matrices are used. By doing this، various samples can be achieved for each cluster and further information are extracted. On the other hand، usage of different measurement matrices may affect the system stability. As a matter of fact، generating the different measurement matrices is not simple and increases complexity into the transmitters. Therefore، it conflicts with the aim of CS theory، reducing complexity into the transmitters. As a result، in the proposed method، the number of the clusters is determined by the number of the producible measurement matrices. Figure 4 shows the schematic of the proposed structure in the satellites. In the stations، we follow HYCA procedure in equation 8 and 9، but the different similar pixels are applied to the both equations. By doing this، we reach to the improved HYCA algorithm. Finally، the proposed structure is shown in the Table 1. To evaluate the proposed method، both real and simulated data have been used in this article. In addition، normalized mean-square error is considered as an error criteria. For the simulated data، in constant measurement sizes، the effects of the additive noise، and for real data، the effects of measurement sizes have been investigated. Besides، the proposed method has been compared with HYCA and C-HYCA and some of the traditional CS based methods. The experimental results show the superiority of the proposed method in terms of signal to noise ratios and the measurement sizes، up to in the simulated data and in the real data، which makes it suitable in the real-world applications.

Spectral unmixing of hyperSpectral images is one of the most important research fields in remote sensing. Recently, the direct use of Spectral libraries in Spectral unmixing is on increase by increasing the availability of the libraries. In this way the Spectral unmixing problem is converted into a sparse regression problem that is time-consuming. This is due to the existence of irrelevant spectra in the library. So these spectra should be removed in some way. In the mentioned approach which is called sparse unmixing, we do not need an endmember extraction algorithm and determination of the number of endmembers priori. Since Spectral libraries usually contain highly correlated spectra, the sparse unmixing approach leads to non-admissible solutions. On the other hand, most of the proposed solutions are not noise-resistant and do not reach to a sufficiently high sparse solution. In this paper, with the purpose of overcoming the problems above, at first a machine learning approach for Spectral library pruning is introduced. The Spectral library is clustered using k-means algorithm based on a simple and efficient new feature space. Subtractive clustering is used for determining the cluster centers of k-means algorithm. Three distance measures, Spectral angle distance, Spectral distance similarity and Spectral correlation similarity tested to select the best for k-means. Then the training data needed to learn a classifier are extracted by adding different noise levels to the clustered spectra. The label of the training data is determined based on the results of Spectral library clustering. After learning the classifier, each pixel of the image is classified using it. This classified image will be used for pruning the Spectral library. For testing the impact of classifier type on the result of unmixing, three classifiers, decision tree classifier, neural networks and k-nearest neighbours are compared. For pruning the library, the spectra with the labels that none of the image pixels belong to, are removed from Spectral library. In this way, the candidate spectra present in the image are extracted. Now, a genetic algorithm will be used for sparse unmixing. Experimental results show that Spectral library pruning enhances the performance of sparse unmixing algorithms. Moreover, using k-nearest neighbor in image classification step, gives better results in the final unmixing process. Genetic algorithm that used for sparse unmixing compared with OMP and SUnSAL algorithms, works well in noisy images.

In this paper, a novel adaptive algorithm for Spectral unmixing in hyperSpectral images (HSIs) is proposed. Many of the existing Spectral unmixing algorithms, under the assumption of the linear model for the Spectral mixing phenomenon, attempt to estimate the signatures of available materials in the observed HSI image. Then, based on the similarity between the estimated Spectral signatures and the available Spectral signatures in the Spectral library, they identify the materials in the HSI and estimate their relative abundances. While the Spectral library, as prior knowledge, has not been directly considered in the founding of existing algorithms, the proposed method is directly concentrated on the Spectral signatures library. Assuming the linear Spectral mixing model, the proposed method takes a set of Spectral signatures which are probably present in the observed HSI. Then, based on a non-statistical approach, the normalized least mean square (NLMS) adaptive algorithm is engaged to estimate a weight vector for each Spectral signature in the selected set in such a way that each weight vector and its corresponding Spectral signature are non-orthogonal whereas the weight vector of each Spectral signature is almost orthogonal to the other Spectral signatures. A synthetic dataset of hyperSpectral images is considered to evaluate the performance of the proposed method. The evaluation results show that the proposed method outperforms its counterparts in low signal to noise ratio (SNR).

The purpose of unmixing in hyperSpectral images is extraction of the end members Spectral signatures and estimation of their related abundance fractions. Most algorithms used for endmember extraction (EE) process, are established on Spectral information without any attention to spatial context and correlation of image pixels. Recently, several algorithms have been developed which utilize spatial and Spectral information with the aim of improving EE and unmixing accuracy. In this paper, a novel spatial Spectral preprocessor is proposed which exploits class map obtained by unsupervised clustering technique and 8th neighborhood window in order to identify pixels located in border regions between two or more clusters and discards not spatially homogenous regions. Afterwards, it calculates Spectral purity weight of not border pixels in order to look for spatially homogenous and Spectrally pure ones using otsu threshold. End members can be extracted rapidly and accurately by means of coupling our proposal with EEs. Our distinct scheme can reduce RMSE of reconstructed image and EE processing time as well as improve a new criterion known as Efficiency regarding the state-of-the-art preprocessors on real hyperSpectral images.

The earth is continually being influenced by some actions such as flood, tornado and human artificial activities. This process causes the changes in land cover type. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Today’ s remote sensing plays key role in geology and environmental monitoring by its high resolution, wide covering and low cost of data receiving from the earth and it has many applications such as change detection. To manage the resources optimally, in local and gloal scale, accuracy and being on-time are very substantial. HyperSpectral images, with thier high ability of Spectral resolution, can improve change detection in result and extract more detail of changes. In this research a new method of change detection for hyperSpectral imagery using the Image-Differencing, Otsu and Spectral unmixing algorithms is presented. The proposed method is presented in three steps: (1) Data correction using image differencing method and data conversion to new computing space. At this space, the changed areas would be more outstanding compare to previous space. (2) the decision about the nature of endmembers is made using Otsu algorithm. (3) spatial resolution enhancement based on abundance map. The proposed method can automatically extract binary change map. In addition, this method provides information about the nature of change in sub-pixel level. To examine the performance of the proposed method, the hyperSpectral imagery (by Hyperion sensors) from Chiangsu fields in china and a simulated data from the AVIRIS sensor were used. The results show the high accuracy of the proposed method in comparison with other methods. Its overall accuracy is more than 93% and its kappa coefficiency is 0. 85 and mean false alarm rates is under 7% for China dataset. And also, the results for second dataset are as follow: the overall accuracy is more than 99% and kappa coefficiency is 0. 82 and mean false alarm rates is under 1%.

unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing.Correlation between bands of HyperSpectral images is the problem that is paid less attention to it in the unmixing algorithms. In this paper, we have proposed a new method for unmixing of HyperSpectral data using semi-nonnegative matrix factorization and principal component analysis. In the proposed method, Spectral and spatial unmixing is performed simultaneously. Physical constraints applied based on Linear Mixing Model. In addition to physical constraints, characteristics of HyperSpectral data have been exploited in the unmixing process. Sparseness of the abundance is one of the important features of HyperSpectral data, which is applied using the nsNMF matrix. In the proposed method update rules is derived using the ALS algorithm. In the final section of this paper, real and synthetic HyperSpectral data is used to verify the effectiveness of the proposed algorithm. Obtained results show the superiority of the proposed algorithm in comparison with some unmixing algorithms.

The hyperSpectral images are studied to extract the Spectral signatures of the elements that comprise the image pixels (end members) and estimate their frequency. The surface reflectance spectrum is considered a linear combination of endmember spectra in linear mixing models. When internal mixing is also important, the linear model is not the answer, and non-linear algorithms should be used. The method used in this research is the generalization and improvement of Nascimento and Fan's bilinear models, known as the generalized bilinear mixing model (BPOGM). This study aims to apply and evaluate this method in the face of data with high mixing and large volumes. Therefore, the data used in this research are Hyperion data of Khoi region, which has good mineral and mineralogical indicators. First, the available pure spectra were extracted using the N-FINDR method. In addition to the excellent compatibility of the N-FINDER method, it has more ability to extract endmembers than the pixel purity index method used in linear separation. In this way, stilbite mineral (representative of zeolite group), vermiculite (representative of mica group), serpentine (representative of olivines of harzburgite and serpentinized ultramafic rocks), chlorite (representative of chlorite group), and quartz were identified. Then, using the BPOGM method, which is a solution method for the bilinear GBM model, the frequency of each end member was calculated, and the distribution map was obtained. The results of the non-linear method comply well with the geological map of the region based on mineralogical interpretations of the lithological facies (average accuracy of 78.25), which is completely acceptable at this stage of exploration work.

Introduction: Non-invasive Fluorescent Reflectance Imaging (FRI) is used for accessing physiological and molecular processes in biological media. The aim of this article is to separate the overlapping emission spectra of quantum dots within tissue-equivalent phantom using SVD, Jacobi SVD, and NMF methods in the FRI mode.Materials and Methods: In this article, a tissue-like phantom and an optical setup in reflectance mode were developed. The algorithm of multiSpectral imaging method was then written in Matlab environment. The setup included the diode-pumped solid-state lasers at 479 nm, 533 nm, and 798 nm, achromatic telescopic, mirror, high pass and low pass filters, and EMCCD camera. The FRI images were acquired by a CCD camera using band pass filter centered at 600 nm and high pass max at 615 nm for the first region and high pass filter max at 810 nm for the second region. The SVD and Jacobi SVD algorithms were written in Matlab environment and compared with a Non-negative Matrix Factorization (NMF) and applied to the obtained images.Results: PSNR, SNR, CNR of SVD, and NMF methods were obtained as 39 dB, 30.1 dB, and 0.7 dB, respectively. The results showed that the difference of Jacobi SVD PSNR with PSNR of NMF and modified NMF algorithm was significant (p<0.0001). The statistical results showed that the Jacobi SVD was more accurate than modified NMF.Conclusion: In this study, the Jacobi SVD was introduced as a powerful method for obtaining the unmixed FRI images. An experimental evaluation of the algorithm will be done in the near future.