Comparing Three Spaceborne Optical Sensors via Fine Scale Pixel-based Urban Land Cover Classification Products

Accessibility to higher resolution earth observation satellites suggests an improvement in the potential for fine scale image classification. In this comparative study, imagery from three optical satellites (WorldView-2, Pleiades and RapidEye) were used to extract primary land cover classesfrom a pixel-based classification principle in a suburban area. Following a systematic working procedure, manual segmentation and vegetation indices were applied to generate smaller subsets to in turn develop sets of ISODATA unsupervised classification maps. With the focus on the land cover classification differences detected between the sensors at spectral level, the validation of accuracies and their relevance for fine scale classification in the built-up environment domain were examined. If an overview of an urban area is required, RapidEye will provide an above average (0.69 k) result with the built-up class sufficiently extracted. The higher resolution sensors such as WorldView-2 and Pleiades in comparison delivered finer scale accuracy at pixel and parcel level with high correlation and accuracy levels (0.65-0.71k) achieved from these two independent classifications

Study and Analysis of Supervised Vs Unsupervised Classification for Remote Sensing Images

Image classification is a procedure to automatically categorize all pixels in an image [9]. Image classification has emerged as a significant tool for investigating digital images [1].Image classification can be defined as the process of reducing an image to information classes. The categorization of image pixels is based on their digital numbers/grey values in one or more spectral bands. The main objective of image classification is to automatically categorize all pixels in a digital image into information classes or themes. The image classification tool for examination of the digital images. Classification is generally divided into two types as supervised classification and unsupervised classification [8]. This paper gives comparative study of Supervised & Unsupervised image classification

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

The accuracy of classified results is often measured in comparison with reference or “ground truth” information. However, in inaccessible or remote natural areas, sufficient ground truth data may not be cost-effectively acquirable. In such cases investigative measures towards the optimisation of the classification process may be required. The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as well as band combinations and the use of vegetation indices. It was found that adding probability thresholds to the classification may provide a measure of suitability regarding training area characteristics and band combinations. The analysis illustrated that finding a balance between training area size and heterogeneity may be fundamental to achieving an optimum classified result.Furthermore, results indicated that the addition of vegetation index values introduced as additional image bands could potentially improve classified products and that threshold outcomes could be used to illustrate confidence levels when mapping classified results

A review of remotely sensed satellite image classification

Satellite image classification has a vital role for the extraction and analysis of the useful satellite image information. This paper comprises the study of the satellite images classification and Remote Sensing along with a brief overview of the previous studies that are proposed in this field. In this paper, the existing work has been explained utilizing the classification techniques on satellite images of Alwar region in India that covers decent land cover features like Vegetation, Water, Urban, Barren, and Rocky regions. The post- implementation of the classification algorithms, the classified image is obtained displaying different classes that are represented by different colours. Each feature is represented by a different colour and can be easily perceived from the image obtained after classification. The focus of this study is on enhancing the classification accuracy by using proper classifiers along with the novel feature extraction techniques and pre-processing steps. Work of different authors is being discussed in a tabular form defining the methods and outcomes of the respective studies

A vector machine based approach towards object oriented classification of remotely sensed imagery

Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. We have adopted certain automatic approaches based on advanced techniques such as Cellular Automata and Genetic Algorithm for improving effective overlap between classes. Proposed methodology has been evaluated in comparison with the conventional approaches with reference to the study area using relevant statistical parameters. Accuracy improvement of the proposed approach may be attributed to the effectiveness in combining spatial and spectral information

COMPARISON OF SUPERVISED CLASSIFICATION TECHNIQUES WITH ALOS PALSAR SENSOR FOR ROORKEE REGION OF UTTARAKHAND, INDIA

The Advanced Land Observing Satellite (ALOS) is developed by the Japanese Aerospace Exploration Agency (JAXA) which was launched in the year 2006 for the Earth observation and exploration purpose. The ALOS was carrying PRISM, AVNIR-2 and PALSAR sensors for this purpose. PALSAR is L-Band synthetic aperture radar (SAR). The PALSAR sensor is designed in a way that it can work in all weather conditions with a resolution of 10 meters. In this research work we have made an investigation on the accuracy obtained from the various supervised classification techniques. We have compared the accuracy obtained by classifying the ALOS PALSAR data of the Roorkee region of Uttarakhand, India. The training ROI’S (Region of Interest) are created manually with the assistance of ArcGIS Earth and for the testing purpose, we have used the Global positioning system (GPS) coordinates of the region. Supervised classification techniques included in this comparison are Parallelepiped classification (PC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper (SAM), Spectral information divergence (SID) and Support vector machine (SVM). Later, through the post classification confusion matrix accuracy assessment test is performed and the corresponding value of the kappa coefficient is obtained. In the result, we have concluded MDC as best in term of overall accuracy with 82.3634% and MLC with a kappa value of 0.7591. Finally, a peculiar relationship is developed in between classification accuracy and kappa coefficient

The role of remote sensing in invasive alien plant species detection and the assessment of removal programs in two selected reserves in the eThekwini Municipality, KwaZulu-Natal Province.

Doctor of Philosophy in Environmental Sciences. University of KwaZulu-Natal, Durban 2016.One of the major current concerns by conservationists is alien invasive plants due to their rapid spread and threat to biodiversity. The detection of Invasive Alien Plant Species (IAPs) can aid in monitoring and managing their invasion on ecosystems. In South Africa approximately 10 million hectares of land have been invaded. To combat this invasion, the Working for Water program was initiated in 1995 aimed at manually removing them. Multispectral imagery can facilitate identification, assess removal initiatives and improve efficiency of IAP removal. The aim of this study is to determine the most appropriate sensor to detect three IAPs (Acacia podalyriifolia, Chromolaena odorata and Litsea glutinosa) and assess clearing programs of these species in two protected areas (Paradise Valley and Roosfontein Nature Reserves) within the eThekwini municipality, in KwaZulu-Natal province, South Africa using remote sensing. The three satellite sensors examined in this study included Landsat 7 ETM+, SPOT 5 and WorldView-2. The study also assessed four image classifiers (Parallelepiped, Maximum Likelihood, Spectral Angle Mapper and Iterative Self Organising Data Analysis Technique) in the detection of the selected IAPs. These sensors and techniques were compared based on their level of accuracy at detecting selected IAPs. The results of the study showed that WorldView-2 imagery and the Maximum Likelihood classifier had the highest overall accuracy (66.67%) , resulting in the successful classification of two (Acacia podalyriifolia and Chromolaena odorata) out of the three target species. This is due to the high spatial resolution of WorldView-2 imagery. This combination was then used to asses clearing of the selected IAPs by examining species distribution and density before and after clearing. Here the overall accuracies for the Paradise Valley and Roosfontein Nature Reserves were successful with accuracies above 85%. The density and distribution of all three IAPs decreased substantially in both sites except for the L. glutinosa species located in the Paradise Valley Nature Reserve which showed no significant decrease. These results show that geospatial data (especially remote sensing data) can be successfully used in both the detection of IAPs and the assessment of their removal

An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images

Artificial neural networks (ANNs) have attracted the attention of researchers in many fields, and have been used to solve a wide range of problems. In the field of remote sensing they have been used in a variety of applications, including land cover mapping, image compression, geological mapping and meteorological image classification, and have generally proved to be more powerful than conventional statistical classifiers, especially when training data are limited and the data in each class are not normally distributed. The use of ANNs requires some critical decisions on the part of the user. These decisions, which are mainly concerned with the determinations of the components of the network structure and the parameters defined for the learning algorithm, can significantly affect the accuracy of the resulting classification. Although there are some discussions in the literature regarding the issues that affect network performance, there is no standard method or approach that is universally accepted to determine the optimum values of these parameters for a particular problem. In this thesis, a feed-forward network structure that learns the characteristics of the training data through the backpropagation learning algorithm is employed to classify land cover features using multispectral, multitemporal, and multisensory image data. The thesis starts with a review and discussion of general principles of classification and the use of artificial neural networks. Special emphasis is put on the issue of feature selection, due to the availability of hyperspectral image data from recent sensors. The primary aims of this research are to comprehensively investigate the impact of the choice of network architecture and initial parameter estimates, and to compare a number of heuristics developed by researchers. The most effective heuristics are identified on the basis of a large number of experiments employing two real-world datasets, and the superiority of the optimum settings using the 'best' heuristics is then validated using an independent dataset. The results are found to be promising in terms of ease of design and use of ANNs, and in producing considerably higher classification accuracies than either the maximum likelihood or neural network classifiers constructed using ad hoc design and implementation strategies. A number of conclusions are drawn and later used to generate a comprehensive set of guidelines that will facilitate the process of design and use of artificial neural networks in remote sensing image classification. This study also explores the use of visualisation techniques in understanding the behaviour of artificial neural networks and the results produced by them. A number of visual analysis techniques are employed to examine the internal characteristics of the training data. For this purpose, a toolkit allowing the analyst to perform a variety of visualisation and analysis procedures was created using the MATLAB software package, and is available in the accompanying CD-ROM. This package was developed during the course of this research, and contains the tools used during the investigations reported in this thesis. The contribution to knowledge of the research work reported in this thesis lies in the identification of optimal strategies for the use of ANNs in land cover classifications based on remotely sensed data. Further contributions include an indepth analysis of feature selection methods for use with high-dimensional datasets, and the production of a MATLAB toolkit that implements the methods used in this study