Feature Based Image Classification by using Principal Component Analysis (original) (raw)

Cloud Classification Based on Images Texture Features

IOP Conference Series: Materials Science and Engineering, 2019

An identification of cloud imagery is part of the cloud observation process which is very important to know the potential for weather changes, especially in the Sultan Hasanuddin airport area. The purpose of this research is to build an artificial intelligence model to identify and classify texture patterns of cloud images. The research used 80 clouds images data contained in the Sultan Hasanuddin Airport area. The data consist of four types of clouds, Altocumulus, Cirrus, Cumulonimbus and Cumulus. In this research, a feature extraction process using Gray Level Co-occurrence Matrix (GLCM) algorithm and Support Vector Machine (SVM) is used for the classification process. We used a set of 4 GLCM features. The 4 selected features are contrast, correlation, energy and homogeneity. Training and testing data using cross validation method with three stages validation. The highest level of accuracy is found in the third stage validation with an accuracy value of 85%.

IJERT-Texture Analysis of Cloud for Weather Information by Neural Network

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/texture-analysis-of-cloud-for-weather-information-by-neural-network https://www.ijert.org/research/texture-analysis-of-cloud-for-weather-information-by-neural-network-IJERTV1IS5183.pdf Classification of different types of cloud images is the primary issue used to forecast precipitation and other weather constituent. A system is presented for cloud classification through satellite images. It involves two main stages, feature extraction and classification. The goal of feature extraction is to determine features from the available channels that make the detection of changes in cloud characteristics easier. The classifier makes the decision on the basis of these features to categorize the image pixels to different cloud types. in this paper, there are two algorithm have been used for cloud classification (i) ANN with features of haar wavelet and (ii) PNN with features of haar wavelet. The use of wavelet coefficient values makes the system more efficient in detecting the minor changes in cloud statistical properties and leading to better classification. Wavelet is used for feature extraction, and artificial neural network and probabilistic neural network used as a classifier. By

Multifeature texture analysis for the classification of clouds in satellite imagery

2003

Abstract The aim of this work was to develop a system based on multifeature texture analysis and modular neural networks that will facilitate the automated interpretation of satellite cloud images. Such a system will provide a standardized and efficient way for classifying cloud types that can be used as an operational tool in weather analysis.

Classification of satellite cloud imagery based on multi-feature texture analysis and neural networks

2001

Abstract The aim of this work was to develop a system based on modular neural networks and multi-feature texture analysis that facilitates the automated interpretation of cloud images. This speeds up the interpretation process and provides continuity in the application of satellite imagery for weather forecasting. A series of infrared satellite images from the geostationary satellite METEOSAT7 were employed.

Cloud Detection using HYGTA Dataset using Principal Component Analysis

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

Clouds got from the camera on the ground are commonly taken using a fisheye point of convergence with a wide overview edge. In any case, the sky has an increasingly broad special range similarly as splendor than standard cameras that can get pictures. Thusly, it is difficult to record the nuances of the whole scene with a run of the mill camera in a singular shot. Overall, the circumsolar district will be introduced to an exorbitant measure of light and the region near the horizon will be introduced to unreasonably negligible light. This makes the division on the cloud for such pictures inconvenient. In this article, we propose an Enhanced Cloud Detection Segmentation (ECDS) strategy, which is a convincing course for isolating the cloud using (HDR) considering the blend of various introduction centers. We depict the route toward making HDR pictures and dispersing new databases for the system for assessment.