Informatics Publishing Limited

K. S. Gunasheela ¹, H. S. Prasantha ¹

Affiliations
1 Department of Electronics and Communication Engineering, NITTE Meenakshi Institute of Technology, IN

Abstract

Hyperspectral image (HSI) processing is one of the key processes in satellite imaging applications. Hyperspectral imaging spectrometers collect huge volumes of data since the image is captured across different wavelength bands in the electromagnetic spectrum. As a result, compression of hyperspectral images is one of the active area in research community from many years. The research work proposes a new compressive sensing based approach for the compression of hyperspectral images called SHSIR (Sparsification of hyperspectral image and reconstruction). The algorithm computes the coefficients of fractional abundance map in matrix setup, which is used to reconstruct the hyperspectral image. To optimize the problem with non-smooth term existence along with large dimensionality, Bregman iterations method of multipliers is used, which converts the difficult optimization problem into simpler cyclic sequence problem. Experimental result demonstrates the supremacy of the proposed method over other existing techniques.

Keywords

Hyperspectral Image, Image Compression, Compressive Sensing.

Full Text

     

H. S. Prasantha ¹, Moon Moon Chatterjee ¹, Pasupuleti Sai Roshitha ¹, V. Roshitha ¹

Affiliations
1 Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, IN

Abstract

Hyperspectral image classification is one of the most emerging form of image classification. It is able to convey information about an image in a more detailed way as compared to RGB or multispectral data. When spectral measurement is performed using hundreds of narrow contiguous wavelength intervals, the resulting image is called a hyperspectral image. Spectral signature of thousands of materials have been measured in the laboratory and gathered into libraries. Library signatures are used as the basis for identification of materials in Hyperspectral Image (HSI) data. We analyze the spectral signature of the image to extract information. In HSI, each pixel is in fact a high dimensional vector typically containing reflectance measurement from hundreds of continuous narrow band spectral channels (FWHM between 2 and 20) and 400-2500 nm wavelength range. The range of spectrum in HSI data extends beyond the visible range. Hyperspectral data processing comes with many stages such as pre-processing, feature reduction, classification and followed by target detection. Various machine learning and deep learning algorithms have been used to classify HSI data where few of them are Support Vector Machine, Convolutional Neural Network, random forest, SSRN, etc. HSI is being used in variety of fields such as agriculture, mining, food quality, soil types, defense etc.

Keywords

Image Classification, Convolutional Neural Network, Support Vector Machine, Hyperspectral.

Full Text