Atmospheric effects on land classification using satellites and their correction.

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat - the remote sensing satellite that have provided the longest continuous record of Earth's surface. The work presented in this thesis is meant to develop a better understanding of atmospheric effects on land classification using satellite data and method of removing them. To do so, the two main atmospheric effects dealt with here are cloud and haze. Detection of cloud and its shadow are carried out using MODIS algorithms due to allowing optimal use of its rich bands. The analysis is applied to Landsat data, in which shows a high agreement with other methods. The thesis then concerns on determining the most suitable classification scheme to be used. Maximum Likelihood (ML) is found to be a preferable classification scheme due to its simplicity, objectivity and ability to classify land covers with acceptable accuracy. The effects of haze are subsequently modelled and simulated as a summation of a weighted signal component and a weighted pure haze component. By doing so, the spectral and statistical properties of the land classes can be systematically investigated, in which showing that haze modifies the class spectral signatures, consequently causing the classification accuracy to decline. Based on the haze model, a method of removing haze from satellite data was developed and tested using both simulated and real datasets. The results show that the removal method is able clean up haze and improve classification accuracy, yet a highly non-uniform haze may hamper its performance

Classification of Landsat 8 Satellite Data Using NDVI Tresholds

This study aims to classify Landsat 8 satellite data using NDVI thresholds. Initially, visible and near infrared bands of Landsat 8 satellite were used to derive Normalized Different Vegetation Index (NDVI) image. Vegetation, non-vegetation and water areas were then analyzed where thresholds for separating them are carefully determined with the aid of ground truth information of the study area. Density slicing was performed in order to separate the image into different land covers. Eventually, color mapping and class labeling were done to complete the classification process. The accuracy of the classified image is then assessed using a confusion matrix where overall classification accuracy and Kappa coefficient are computed. The result shows that NDVI-based classification is able to classify the Landsat 8 satellite data with a high accuracy

Privacy, Ethics, And Security On Social Media: An Islamic Overview

Privacy is one of the most critical fields in recent years since the presence of social media that is increasingly eroding the fundamental human right to privacy. This phenomenon raises many people's concerns about the future o privacy. Commonly discussed by many people are privacy, ethics, and security, including the extent to which they are being taken seriously by designers and users social media. The discussion is not only from the perspective of western culture but also from the religious point of view. The purposes ot this paper is to investigate the privacy, ethics, and security concerns in social media from an Islamic overview. Towards this, we begin by introducing the Qur'an and Sunnah as the life guidelines for humanity. We then highlight the mention of privacy, ethics, and security in both these guidelines. Then we explain it in sequence in each section accompanied by the existing problems in social media. Finally, we discuss the future of privacy in social media

Pengaruh kualitas layanan dan store atmosphere terhadap customer loyalty

This study aims to determine consumer responses regarding the effect of service quality and store atmosphere on consumer loyalty at the Surjoy coffee shop in Bandung. The population in this study were true coffee consumers in the city of Bandung as many as 101 people, where the sample in this study was also the population. Data were obtained through interviews, observations, literature studies, and distributing questionnaires. Multiple linear analysis. The method used is descriptive verification with a quantitative approach. The results in this study indicate that there is an influence between store atmosphere and service quality on consumer loyalty. This result is supported by Dahmiri's research (2020) that there is a significant effect of these two variables on consumer loyalty. This study emphasizes that these two variables must be managed properly by the organization, especially when competition is increasing

Analysis Of Signal To Noise Ratio On Restored Multispectral Data

An analysis of signal to noise ratio (SNR) of restored multispectral data is reported. The data comes from multispectral satellite sensor and has undergone a restoration process due to the degradation by atmospheric haze. The restoration involves subtracting haze mean due to haze scattering and filtering haze randomness due to haze spatial variability. The results shows that the SNR of restored data after Gaussian filtering is higher than average and median filtering. The improvement of SNR at short and moderate visibilities is more significant than good visibilities

Non-Redundant Implicational Base of Many-Valued Context Using SAT

Some attribute implications in an implicational base of a derived context of many-valued context can be inferred from some other attribute implications together with its scales. The scales are interpretation of some values in the many-valued context therefore they are a prior or an existing knowledge. In knowledge discovery, the such attribute implications are redundant and cannot be considered as new knowledge. Therefore the attribute implicational should be eliminated. This paper shows that the redundancy problem exists and formalizes a model to check the redundancy

Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN

Euphotic Depth Zone Variation in Peninsular Malaysia Maritime

This study is conducted in Peninsular Malaysia maritime to investigate the euphotic zone depth (Zeu) variation and the possible suspended matter that may contribute to the variation. The Zeu data were acquired from the MODIS Aqua satellite from November 2002 to September 2013. The result shows that the Zeu along the Malaysia maritime are highly seasonal-dependent. The lowest Zeu values are observed during the northeast monsoon season (NEMS) in the east coast Peninsular Malaysia and during the southwest monsoon season (SWMS) for the west coast area. Chlorophyll- a (Chl-a) and colored dissolved organic matter (CDOM) are found to be the contributing factors for the coastal line and open water area. While, sediment only contributes to the area located along the coastal line where lower Zeu values are observed

Survey On Nudity Detection: Opportunities And Challenges Based On ‘Awrah Concept In Islamic Shari’a

The nudity or nakedness which known as awrah in Islam is part of the human body which in principle should not be seen by other people except those qualified to be her or his mahram or in an emergency or urgent need.Nudity detection technique has long been receiving a lot of attention by researchers worldwide due to its importance particularly to the global Muslim community. In this paper, the techniques were separated into four classifications namely methods based on body structure, image retrieval, the features of skin region, and bag-of-visual-words (BoVW). All of these techniques are applicable to some areas of skin on the body as well as on the sexual organs that should be visible to determine nude or not. While the concept of nakedness in Islamic Shari'a has different rules between men and women, such as the limit of male ‘awrah is between the navel and the knees, while the limit of female ‘awrah is the entire body except the face and hands which should be closed using the hijab. In general, existing techniques can be used to detect nakedness concerned bythe Islamic Shari'a. The selection ofhese techniques are employed based on the areas of skin on the body as well as or the sexual organs to indicate whether it falls to thenude category or not. While in Islamic Shari'a, different 'awrah rules are required for men and women such as the limit 'awrah, the requirements of clothes as cover awrah, and kinds of shapes and shades of Hijabs in various countries (for women only). These problems are the opportunities and challenges for the researcher to propose an ‘awrah detection technique in accordance with the Islamic Shari'a

Classification Techniques In Blood Donors Sector – A Survey

This paper focuses on the classification and the recent trends associated with it. It presents a survey of the classification system and clarifies how classification and data mining are related both to each other. Classification is arranging the blood donor dataset into the predefined group and helpful to predict group membership for data instances. This enables users to search target donors become easier because the blood stocks always required replacing expired stocks after a certain period and useful in emergency demands such as surgery and blood transfusion. This paper has also sought to identify the research area in classification to fulfill gaps where further work can be carried on