Spectral Unmixing Using Improved HYCA Algorithm

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Khajeh Rayeni Farshid; GHASEMIAN HASSAN

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Journal: SIGNAL AND DATA PROCESSING Year:2017 | Volume:14 | Issue:3 (serial 33) Page(s): 37-50

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Title

Spectral Unmixing Using Improved HYCA Algorithm

Author(s)

Khajeh Rayeni Farshid | GHASEMIAN HASSAN | Issue Writer Certificate

Keywords

Compressive Sensing (CS)Q1

HYCA algorithmQ1

hyperspectral imagingQ2

spatial and spectral informationQ1

spectral unmixingQ1

Abstract

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.

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APA: Copy

Khajeh Rayeni, Farshid, & GHASEMIAN, HASSAN. (2017). Spectral Unmixing Using Improved HYCA Algorithm. SIGNAL AND DATA PROCESSING, 14(3 (serial 33) ), 37-50. SID. https://sid.ir/paper/160704/en

Vancouver: Copy

Khajeh Rayeni Farshid, GHASEMIAN HASSAN. Spectral Unmixing Using Improved HYCA Algorithm. SIGNAL AND DATA PROCESSING[Internet]. 2017;14(3 (serial 33) ):37-50. Available from: https://sid.ir/paper/160704/en

IEEE: Copy

Farshid Khajeh Rayeni, and HASSAN GHASEMIAN, “Spectral Unmixing Using Improved HYCA Algorithm,” SIGNAL AND DATA PROCESSING, vol. 14, no. 3 (serial 33) , pp. 37–50, 2017, [Online]. Available: https://sid.ir/paper/160704/en

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