Abstract
Spectral analysis of remotely sensed images provide the required information accurately even for small targets. Hence Hyperspectral imaging is being used which follows the technique of dividing images into bands. These Hyperspectral images find their applications in agriculture, biomedical, marine analysis, oil seeps detection etc. A Hyperspectral image contains many spectra, one for each individual point on the sample’s surface and in this project the required target on the Hyperspectral image is going to be detected and classified. Hyperspectral remote sensing image classification is a challenging problem because of its high dimensional inputs, many class outputs and limited availability of reference data. Therefore some powerful techniques to improve the accuracy of classification are required. The objective of our project is to reduce the dimensionality of the Hyperspectral image using Principal Component Analysis followed by classification using Neural Network. The project is to be implemented using MATLAB.
Highlights
A new generation of remote sensing instruments with very high spectral resolution, called imaging spectrometers or Hyperspectral imaging sensors have been developed to uncover subtle material substances that generally cannot be resolved by multispectral sensors
Hyperspectral imaging could be used for identifying or detecting a target which is very much smaller and which could be said to be present in sub pixel level
This paper reveals that Principal Component Analysis is the useful feature extraction technique for Hyperspectral image classification
Summary
A new generation of remote sensing instruments with very high spectral resolution, called imaging spectrometers or Hyperspectral imaging sensors have been developed to uncover subtle material substances that generally cannot be resolved by multispectral sensors. Hyperspectral imaging could be used for identifying or detecting a target which is very much smaller and which could be said to be present in sub pixel level. In this way this detection technique could be used in various applications such as special species detection in agriculture, detecting toxic/metal waste in environmental monitoring, rare minerals in geology, drug tracking in law enforcement, diseases in crops, and some other dairy applications. Reducing the amount of data involved or selecting the relevant bands associated with a particular application from the entire data set becomes a unique, yet primary task for Hyperspectral image analysis.
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