High-Resolution Satellite Imagery Classification for Urban Form Detection

Mapping urban form at regional and local scales is a crucial task for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. Remotely sensed imagery is ideally used to monitor and detect urban areas that occur frequently as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into urban form map using the existing methods of manual interpretation and parametric image classification digitally. In this work, classification techniques of high-resolution satellite imagery were used to map 50 selected cities of study of the National Urban System in Mexico, during 2015–2016. In order to process the information, 140 RapidEye Ortho Tile multispectral satellite imageries with a pixel size of 5 m were downloaded, divided into 5 × 5 km tiles and then 639 tiles were generated. In each (imagery or tile), classification methods were tested, such as: artificial neural networks (RNA), support vector machines (MSV), decision trees (AD), and maximum likelihood (MV); after tests, urban and nonurban categories were obtained. The result is validated with an accuracy method that follows a stratified random sampling of 16 points for each tile. It is expected that these results can be used in the construction of spatial metrics that explain the differences in the Mexican urban areas

Benchmark of machine learning methods for classification of a Sentinel-2 image

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performanc

The Random Forest Algorithm with Application to Multispectral Image Analysis

The need for computers to make educated decisions is growing. Various methods have been developed for decision making using observation vectors. Among these are supervised and unsupervised classifiers. Recently, there has been increased attention to ensemble learning--methods that generate many classifiers and aggregate their results. Breiman (2001) proposed Random Forests for classification and clustering. The Random Forest algorithm is ensemble learning using the decision tree principle. Input vectors are used to grow decision trees and build a forest. A classification decision is reached by sending an unknown input vector down each tree in the forest and taking the majority vote among all trees. The main focus of this research is to evaluate the effectiveness of Random Forest in classifying pixels in multispectral image data acquired using satellites. In this paper the effectiveness and accuracy of Random Forest, neural networks, support vector machines, and nearest neighbor classifiers are assessed by classifying multispectral images and comparing each classifier\u27s results. As unsupervised classifiers are also widely used, this research compares the accuracy of an unsupervised Random Forest classifier with the Mahalanobis distance classifier, maximum likelihood classifier, and minimum distance classifier with respect to multispectral satellite data

Mapping and modelling the habitat of giant pandas in Foping Nature Reserve, China

The fact that only about 1000 giant pandas and 29500 km2 of panda habitat are left in the west part of China makes it an urgent issue to save this endangered animal species and protect its habitat. For effective conservation of the giant panda and its habitat, a thorough evaluation of panda habitat and panda-habitat relationship based on each individual panda nature reserve is necessary and important. Mapping has been an effective approach for wildlife habitat evaluation and monitoring. Therefore, mapping is also an important step in evaluating panda habitat and further being used to analyse panda-habitat relationship. Only Foping Nature Reserve is focused in this study. The objectives of this research are: (1) to develop a highly accurate mapping method which can map panda habitat using multi-type data (remote sensing data, digital terrain data, radio tracking data, and plot data from field survey) in GIS; (2) to study panda movement patterns; and (3) to analyse panda habitat use and selection.A general introduction to the thesis is given in Chapter 1. It describes the research background and problems, and formulates the objectives and outlines of the research.In order to find a potentially better mapping algorithm, three algorithms (i.e., parallelepiped algorithm, maximum likelihood algorithm, and backpropagation neural network algorithm) were evaluated using simulated data sets as well as the remotely sensed imagery in Chapter 2. The discrimination capability of the backpropagation neural network algorithm was also explored in this chapter. The results show that the backpropagation neural network classifier has completely discriminated two spectrally discrete classes, and obtained a significantly higher mapping accuracy than the other two algorithms using both simulated data sets and remotely sensed imagery.Since different mapping techniques have complementary capabilities, two integrated mapping approaches were developed in Chapter 3 so as to combine the advantages from different mapping algorithms.' The "expert "system algorithm based on Bayesian probability theory was firstly discussed in this chapter. One integrated mapping approach is the consensus builder, which is used to adjust classification outputs in the case of a discrepancy in classification between maximum likelihood, expert system and neural network classifiers. The second approach is termed the integrated expert system and neural network classifier (ESNNC), which integrates the output of the rule-based expert system classifier with the backpropagation neural network classifier (BPNNC) before and after running the neural network system. The ESNNC produced maps with the highest accuracy compared to not only the individual backpropagation neural network classifier, expert system classifier and maximum likelihood classifier, but also the combined classifier - consensus builder.The giant panda habitat in Foping Nature Reserve was mapped using the ESNNC in Chapter 4. Two categories of panda habitat types were defined and mapped: ground- cover-based potential panda habitat types and suitability-based panda habitat types. Mapping the ground-cover-based potential panda habitat types used only field survey plot data with records of ground cover types, while mapping the suitability-based panda habitat types used not only the field survey plot data but also radio tracking data - meaning actual panda occurrence. Results show that both the ground cover based and the suitability-based panda habitat types were mapped with significantly higher accuracy compared with non-integrated classifiers: expert system, neural network and maximum likelihood classifiers. The classified maps show us that 97% of the nature reserve is covered by forest and about 68% of the nature reserve is a suitable habitat for pandas.With radio tracking data, panda movement patterns were studied in Chapter 5. The use of GIS combined with statistical tools to thoroughly analyse radio-tracking data to reveal panda movement patterns is a new aspect in panda ecological research. Results show that pandas in Foping NR occupied two distinct seasonal activity ranges (i.e., winter and summer activity ranges) and had a regular seasonal movement between the winter range below 1950 m, and the summer range above 2160 m. Pandas spent about 8 days (from June 7 to 15) to climb up to the summer habitats, while they took about 36 days (from September 1 to October 6) to descend to the winter habitats. Consequently, they spent about 243 days in their winter activity range and about 78 days in the summer activity range. Research also shows that pandas travelled shorter distances with small variation in October, December, January, February, July and August, and longer distances with larger variation in March, April, May, June and September.Analysis of wildlife habitat use and selection has been a common and important aspect of wildlife science. Little is known about panda habitat use and selection, especially about the relationship between panda presence and structures of the bamboo layer as well as the tree layer. In Chapter 6, tracking data were used to analyse panda habitat use and selection, and 110 field survey plots with measured information were analysed to identify differences of characteristics between panda-presence and panda-absence habitats. In the winter range, pandas spend more time in deciduous broadleaf forest with an elevation range of 1600 to 1800 m, a slope range of 10 to 20 degrees, and south- facing slopes. In the summer range, they use more conifer forest with an elevation range of 2400 to 2600 m, a slope range of 20 to 30 degrees. In Bashania fargesii bamboo areas with panda presence, bamboo groves have shorter and denser bamboo culms from different ages. In Fargesia Spathacea bamboo areas with panda presence, bamboo groves have higher coverage, taller and thicker bamboo culms, which are mainly one to two years old.Conclusions from the whole study are summarised in Chapter 7. It is recommended that the whole approach used in this study mayor should be applied to the neighbouring panda nature reserves in the Qinling Mountains. The uncompleted research tasks are discussed in this chapter. Therefore, this chapter has shown some possible research topics for future panda conservation studies.In summary, the following are the main findings of this research:Backpropagation neural network classifier can discriminate two classes with no overlap in their feature space.The integrated expert system and neural network classifier was developed and applied in mapping panda habitats, and obtained significantly higher overall mapping accuracy than non-integrated classifiers: expert system classifier, backpropagation neural network classifier, and maximum likelihood classifier.The integrated expert system and neural network classifier can identify a class that has only few samples, while the traditional maximum likelihood classifier fails because insufficient samples cannot form the statistical parameters to run the classification.The integrated expert system and neural network classifier successfully classified panda habitat types using multi-type input data: remote sensing data (TM1-S and 7), terrain data (elevation, slope gradient and slope direction), social data (settlement distance), radio-tracking data, as well as field survey plot data.Radio-tracking data were involved in mapping panda habitat for the first time. They can be a good indicator of suitable habitats for pandas.The movement pattern of pandas in Foping Nature Reserve was thoroughly studied and revealed using GIS combined with statistical tools. Pandas spent a very short period of 8 days in June to move from winter to summer habitats, while they used more than one month in September to descend from summer to winter habitats.The finding that pandas in Foping Nature Reserve have a shorter movement distance and a small activity range in January and February indicates these two months may be a good time for conducting a panda population survey.Panda habitat maps produced by the integrated expert system and neural network classifier with higher accuracy have been used for analysing panda habitat use and selection. Pandas in Foping Nature Reserve mainly select deciduous broadleafforest in the winter activity range, and select conifer forest and Fargesia bamboogroves in the summer activity range.The structure parameters of the bamboo layer in panda-presence habitats are significantly different from those in panda-absence habitats.</UL

Self Designing Pattern Recognition System Employing Multistage Classification

Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one. In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three. The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given. A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise