Object based classification of high resolution remote sensing image using HRSVM-CNN classifier

Remote Sensing (RS) implies the science of recognition of earth surface features utilizing electromagnetic radiation. A lot of images encompass a higher spatial and also radiometric resolution. To obtain the required information from the various satellite images, efficient classification and also im...

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Bibliographic Details
Published in:	European journal of remote sensing Vol. 53; no. sup1; pp. 16 - 30
Main Authors:	M., Poomani Alias Punitha, Sutha, J.
Format:	Journal Article
Language:	English
Published:	Cagiari Taylor & Francis 22-06-2020 Taylor & Francis Ltd Taylor & Francis Group
Subjects:	Algorithms Classification Classifiers Earth surface Electromagnetic radiation Fuzzy CMeans (FCM) Gray Level Cooccurrence Matrices (GLCM) Histograms Hybrid contrast limited adaptive histogram equalizationDiscrete cosine transform (HCLAHEDCT) Hybrid regression support vector machineconvolutional neural network (HRSVM_CNN) Image classification Image processing Image resolution Image segmentation Modified region based image segmentation (MRBI) Natural vegetation Object recognition Radiation Radiometric resolution Remote sensing Satellite imagery Satellites
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Summary:	Remote Sensing (RS) implies the science of recognition of earth surface features utilizing electromagnetic radiation. A lot of images encompass a higher spatial and also radiometric resolution. To obtain the required information from the various satellite images, efficient classification and also image processing is needed. Also, in existent works, the accuracy is low. To overcome such drawbacks, this paper proposed an object-based classification (OBC) of high-resolutions RS image (HRRSI) using HRSVM-CNN classifier. Initially, the input image is improved utilizing hybrid CLAHE-DCT in the preprocessing stage. The improved image is then segmented by using MRBIS algorithm. The MRBIS segmentation has five steps, such as seed selection using seed generation, FCM, region growing (RG), segment initialization, and also region merging. In the RG step, the image is segmented as regions centered on space, spectral, location, and area. Subsequently, the LTrP, color histogram, GLCM, GLDM, edge feature, shape features are extorted as of the segmented image. Finally, the classifier named as HRSVM-CNN is utilized to classify the input as four classes, for instance, Natural Vegetation, Road, Residential, and Water-bodies. The performance proffered by the proposed segmentation and classification techniques is contrasted to the prevailing techniques centered on statistical measures.
ISSN:	2279-7254 2279-7254
DOI:	10.1080/22797254.2019.1680259