  Hyperspectral image (HSI) classification plays a significant in the field of
remote sensing due to its ability to provide spatial and spectral information.
Due to the rapid development and increasing of hyperspectral remote sensing
technology, many methods have been developed for HSI classification but still a
lack of achieving the better performance. A Frost Filtered Scale-Invariant
Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC)
technique is introduced for classifying the hyperspectral image with higher
accuracy and minimum time consumption. The FFSIFT-MLPC technique performs three
major processes, namely preprocessing, feature extraction and classification
using multiple layers. Initially, the hyperspectral image is divided into
number of spectral bands. These bands are given as input in the input layer of
perceptron. Then the Frost filter is used in FFSIFT-MLPC technique for
preprocessing the input bands which helps to remove the noise from
hyper-spectral image at the first hidden layer. After preprocessing task,
texture, color and object features of hyper-spectral image are extracted at
second hidden layer using Gaussian distributive scale-invariant feature
transform. At the third hidden layer, Euclidean distance is measured between
the extracted features and testing features. Finally, feature matching is
carried out at the output layer for hyper-spectral image classification. The
classified outputs are resulted in terms of spectral bands (i.e., different
colors). Experimental analysis is performed with PSNR, classification accuracy,
false positive rate and classification time with number of spectral bands. The
results evident that presented FFSIFT-MLPC technique improves the hyperspectral
image classification accuracy, PSNR and minimizes false positive rate as well
as classification time than the state-of-the-art methods.

Computer Vision and Pattern Recognition;Image and Video Processing