The significance of Real-time, biomedical and satellite Image Processing in understanding the objects &amp; application to Computer Vision

Santha, T.; Babu, Mohana Maniganda

doi:10.1109/icetech.2016.7569333

Cited by 4 publications

(1 citation statement)

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“…There are three main image classification techniques. [4] 1) Image Classification Techniques in Remote Sensing: a) Unsupervised image classification b) Supervised image classification c) Object-based image analysis [5] Pixels are the smallest unit represented in an image. Image classification uses the reflectance statistics for individual pixels.…”

Section: A Image Classification In Remote Sensingmentioning

confidence: 99%

Enhancement of the Satellite Images using ACO and DNN Approach

Kaur¹

2018

IJRASET

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Image classification is a vital technique used to remote-sensing for the pattern identification and design analysis of the satellite information in the form of images. Recent years, several kinds of classification methods like as a minimum distance, SVM and ANN etc. Image Segmentation performs in segmenting the satellite images into sub-clusters which of our interest which could be studied individually. Satellite images is one of the world wide fields in segmentation. Image Segmentation provides in extract the same features from the images depends on their shape, color , intensity and clusters then together using several segmentation methods. Before evaluating the segmentation on satellite images, sometimes image blurring must be remove the interference from the images and certain edge detection are Sobel and canny etc are in use. In prior work, using hybrid method and clustering approach for land cover mapping for trees, building and roads etc. It works with the help phase is pre-processing process to create the image the satellite image suitable for segmentation. The image is process for segmentation using the genetic algorithm (GABC) method that is developed by crossbreed method the GA and ABC to obtain the efficient segmentation in satellite image and classified using NN. In research work implemented the new methods to classify and perform the segmentation of the satellite image in reduce the time interval rate and computation the better accuracy rate based on DNN (Deep Neural Network). We design a framework in satellite land images classifies using MATLAB 2016a simulation tool. Computing the metrics are MSE (mean Square Error Rate), DB-Index , Accuracy , RMSE and compared with the existing metrics (MSE, DBI and XDBI).

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Section: A Image Classification In Remote Sensingmentioning

confidence: 99%