• Nidhi Kapadiya
• Daxa V. Vekariya
• Pratixa Badeliya
• Dr. Vipul Vekariya

The objective of the image classification is to extract the patterns, tremendous information present in the image in form of classes .Satellite image classification process involves grouping the image pixel values into meaningful categories. Several satellite image classification methods and techniques are available but texture extraction plays an important and vital role for efficient classification. This system proposes a novel method for Satellite Imagery Classification .Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Gabor filters with different frequencies and orientations may be helpful for extracting useful features from an image. They have rich applications in image processing, especially in feature extraction for texture analysis and segmentation .Fuzzy c means algorithm classifying the images into different classes such as Barren Land,Agriculture,Forest,Water. Our proposed method is a combination of LNIP(Local neighborhood Intensity Pattern),Gabor filter for texture feature extraction and then image is classified using an unsupervised Fuzzy c-means(FCM) classification algorithm.

Satellite Image Classification, Texture feature,LNIP, Gabor filter ,FCM

"Texture Feature Based Satellite Image Classification Using LNIP and Gabor filter", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.473-478, May-2019, Available :http://www.jetir.org/papers/JETIR1905L69.pdf

"Texture Feature Based Satellite Image Classification Using LNIP and Gabor filter", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp473-478, May-2019, Available at : http://www.jetir.org/papers/JETIR1905L69.pdf