Image Classification in Remote Sensing

One of the most important functions of remote sensing data is the production of Land Use and Land Cover maps and thus can be managed through a process called image classification. This paper looks into the following components related to the image classification process and procedures and image classification techniques and explains two common techniques K-means Classifier and Support Vector Machine (SVM). Keywords: Remote Sensing, Image Classification, K-means Classifier, Support Vector Machin

Change Detection Process and Techniques

Land use / land cover changes studies have become very interesting over the past decades through using remote sensing because of the availability of a suite of sensors operating at various imaging scales and scope of using various techniques as well as considered the good ways for effective monitoring and accurate land use /land cover changes. This paper looks into the following aspects related to the remote sensing technology, change detection process and techniques for land cover changes, and factor affecting change detection techniques and considerations. Keywords: Remote Sensing, Land Use / Land Cover, Change Detectio

SCS+C Topographic Correction to Enhance SVM Classification Accuracy

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4 % which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification 90.1 % accuracy compared to the uncorrected image 86.2 % for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification

Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen.

An effective and efficient planning of an urban growth and land use changes and its impact on the environment requires information about growth trends and patterns amongst other important information. Over the years, many urban growth models have been developed and used in the developed countries for forecasting growth patterns. In the developing countries however, there exist a very few studies showing the application of these models and their performances. In this study two models such as cellular automata (CA) and the SLEUTH models are applied in a geographical information system (GIS) to simulate and predict the urban growth and land use change for the City of Sana’a (Yemen) for the period 2004–2020. GIS based maps were generated for the urban growth pattern of the city which was further analyzed using geo-statistical techniques. During the models calibration process, a total of 35 years of time series dataset such as historical topographical maps, aerial photographs and satellite imageries was used to identify the parameters that influenced the urban growth. The validation result showed an overall accuracy of 99.6 %; with the producer’s accuracy of 83.3 % and the user’s accuracy 83.6 %. The SLEUTH model used the best fit growth rule parameters during the calibration to forecasting future urban growth pattern and generated various probability maps in which the individual grid cells are urbanized assuming unique “urban growth signatures”. The models generated future urban growth pattern and land use changes from the period 2004–2020. Both models proved effective in forecasting growth pattern that will be useful in planning and decision making. In comparison, the CA model growth pattern showed high density development, in which growth edges were filled and clusters were merged together to form a compact built-up area wherein less agricultural lands were included. On the contrary, the SLEUTH model growth pattern showed more urban sprawl and low-density development that included substantial areas of agricultural lands

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

A geospatial solution using a TOPSIS approach for prioritizing urban projects in Libya

The world population is growing rapidly; consequently, urbanization has been in an increasing trend in many developing cities around the globe. This rapid growth in population and urbanization have also led to infrastructural development such as transportation systems, sewer, power utilities and many others. One major problem with rapid urbanization in developing/third-world countries is that developments in mega cities are hindered by ineffective planning before construction projects are initiated and mostly developments are random. Libya faces similar problems associated with rapid urbanization. To resolve this, an automating process via effective decision making tools is needed for development in Libyan cities. This study develops a geospatial solution based on GIS and TOPSIS for automating the process of selecting a city or a group of cities for development in Libya. To achieve this goal, fifteen GIS factors were prepared from various data sources including Landsat, MODIS, and ASTER. These factors are categorized into six groups of topography, land use and infrastructure, vegetation, demography, climate, and air quality. The suitability map produced based on the proposed methodology showed that the northern part of the study area, especially the areas surrounding Benghazi city and northern parts of Al Marj and Al Jabal al Akhdar cities, are most suitable. Support Vector Machine (SVM) model accurately classified 1178 samples which is equal to 78.5% of the total samples. The results produced Kappa statistic of 0.67 and average success rate of 0.861. Validation results revealed that the average prediction rate is 0.719. Based on the closeness coefficient statistics, Benghazi, Al Jabal al Akhdar, Al Marj, Darnah, Al Hizam Al Akhdar, and Al Qubbah cities are ranked in that order of suitability. The outputs of this study provide solution to subjective decision making in prioritizing cities for development