# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 1 Introduction  
Ongoing interest within the realm of remote sensing applications has led to the dramatic development of hyperspectral sensors, which possess high spatial and spectral resolution, having hundreds or thousands of bands. In particular, the stationary imaging Fourier transform spectrometer (SIFTS) has been given considerable attention because it does not employ any moving parts [1], and much effort has been invested in its development for use in airborne and spaceborne missions, such as the spatially modulated imaging Fourier transform spectrometer (SMIFTS), the Fourier transform visible hyperspectral imager (FTVHSI), the Fourier transform hyperspectral imager (FTHSI), the digital array scanned interferometer (DASI), the high-efficiency hyperspectral imager (HEHSI), and the hyperspectral imaging system SYSIPHE [2]. However, traditional SIFTSs all have a slit that greatly reduces the utilization of energy [3]. The large aperture static imaging spectrometer (LASIS) is a temporally-spatially modulated imaging interferometer that was developed in the late 1990s [4, 5]. Because the slit is eliminated in the optical system of the LASIS, it has a much higher throughput than conventional SIFTSs. Additionally, this system offers benefits such as a high luminous flux, spectral linearity, a lightweight structure, and a broad spectral range while effectively resolving the challenge of maintaining high optical flux and stability. In recent years, several interferometers that use LASIS-like principles have been proposed, such as the aerospace leap-frog imaging static interferometer for earth observation (ALISEO) [6], the long wavelength infrared (LWIR) hyperspectral imager [7], and the large-aperture spatial heterodyne imaging spectrometer (LASHIS) [8, 9, 10]. As is well known, the hyperspectral imager obtains a large volume of three-dimensional data cubes consisting of one spectral and two spatial dimensions, and the transmission and storage of the huge amounts of data present special challenges. Therefore, efficient compression techniques are required to resolve this problem. Considerable research has been conducted in the area of hyperspectral data compression [11; 12; 13; 14; 15; 16]. Nevertheless, LASIS data and traditional hyperspectral data exhibit distinct discrepancies. A frame of LASIS data contains spatial information in one spatial direction, and interference information is superimposed on spatial information in the other spatial direction, which is also the optical path difference (OPD) direction. Each row of the 2D detector array corresponds with a different OPD band. Each column of the array samples a different spatial location, and the array, therefore, has a spatial-OPD layout. However, the unique characteristics of LASIS data introduce new problems for data compression. Because of the practical applications of LASIS data in engineering, studies have been conducted to identify compression methods for this new type of data. [PERSON] [17] proposed a compression technique that utilizes the rectangle region of interest (ROI) coding from EBCOT, which does not lift the coefficients of the wavelet domain and is complemented by adaptively computing the distortion in the code stream organization. [PERSON] [18] employed a matching algorithm in the wavelet domain to reduce the redundancy in two successive LASIS frames and then applied a new ROI coding algorithm in the code stream organization stage to substitute the DWT suggested by JPEG2000. [PERSON] [19] improved the weighted rate-distortion optimization for SPHHT after analyzing the correlation between the distortion in the Fourier domain and compression in the spatial domain. To improve the performance of this method, [PERSON] [20] applied the partial SPHHT with classified weighted rate-distortion optimization. [PERSON] [21] introduced a wavelet transform based on three-dimensional orientation prediction in the compression approach of LASIS. However, the unique features of LASIS have not been fully explored.