Using pixel-based and object-based methods to classify urban hyperspectral features

Object-based image analysis methods have been developed recently. They have since become a very active research topic in the remote sensing community. This is mainly because the researchers have begun to study the spatial structures within the data. In contrast, pixel-based methods only use the spectral content of data. To evaluate the applicability of object-based image analysis methods for land-cover information extraction from hyperspectral data, a comprehensive comparative analysis was performed. In this study, six supervised classification methods were selected from pixel-based category, including the maximum likelihood (ML), fisher linear likelihood (FLL), support vector machine (SVM), binary encoding (BE), spectral angle mapper (SAM) and spectral information divergence (SID). The classifiers were conducted on several features extracted from original spectral bands in order to avoid the problem of the Hughes phenomenon, and obtain a sufficient number of training samples. Three supervised and four unsupervised feature extraction methods were used. Pixel based classification was conducted in the first step of the proposed algorithm. The effective feature number (EFN) was then obtained. Image objects were thereafter created using the fractal net evolution approach (FNEA), the segmentation method implemented in eCognition software. Several experiments have been carried out to find the best segmentation parameters. The classification accuracy of these objects was compared with the accuracy of the pixel-based methods. In these experiments, the Pavia University Campus hyperspectral dataset was used. This dataset was collected by the ROSIS sensor over an urban area in Italy. The results reveal that when using any combination of feature extraction and classification methods, the performance of object-based methods was better than pixel-based ones. Furthermore the statistical analysis of results shows that on average, there is almost an 8 percent improvement in classification accuracy when we use the object-based methods

Study of Mobile Robot Operations Related to Lunar Exploration

Mobile robots extend the reach of exploration in environments unsuitable, or unreachable, by humans. Far-reaching environments, such as the south lunar pole, exhibit lighting conditions that are challenging for optical imagery required for mobile robot navigation. Terrain conditions also impact the operation of mobile robots; distinguishing terrain types prior to physical contact can improve hazard avoidance. This thesis presents the conclusions of a trade-off that uses the results from two studies related to operating mobile robots at the lunar south pole. The lunar south pole presents engineering design challenges for both tele-operation and lidar-based autonomous navigation in the context of a near-term, low-cost, short-duration lunar prospecting mission. The conclusion is that direct-drive tele-operation may result in improved science data return. The first study is on demonstrating lidar reflectance intensity, and near-infrared spectroscopy, can improve terrain classification over optical imagery alone. Two classification techniques, Naive Bayes and multi-class SVM, were compared for classification errors. Eight terrain types, including aggregate, loose sand and compacted sand, are classified using wavelet-transformed optical images, and statistical values of lidar reflectance intensity. The addition of lidar reflectance intensity was shown to reduce classification errors for both classifiers. Four types of aggregate material are classified using statistical values of spectral reflectance. The addition of spectral reflectance was shown to reduce classification errors for both classifiers. The second study is on human performance in tele-operating a mobile robot over time-delay and in lighting conditions analogous to the south lunar pole. Round-trip time delay between operator and mobile robot leads to an increase in time to turn the mobile robot around obstacles or corners as operators tend to implement a `wait and see\u27 approach. A study on completion time for a cornering task through varying corridor widths shows that time-delayed performance fits a previously established cornering law, and that varying lighting conditions did not adversely affect human performance. The results of the cornering law are interpreted to quantify the additional time required to negotiate a corner under differing conditions, and this increase in time can be interpreted to be predictive when operating a mobile robot through a driving circuit

Supervised classification of landforms in Arctic mountains

Erosional and sediment fluxes from Arctic mountains are lower than for temperate mountain ranges due to the influence of permafrost on geomorphic processes. As permafrost extent declines in Arctic mountains, the spatial distribution of geomorphic processes and rates will change. Improved access to high‐quality remotely sensed topographic data in the Arctic provides an opportunity to develop our understanding of the spatial distribution of Arctic geomorphological processes and landforms. Utilizing newly available Arctic digital topography data, we have developed a method for geomorphic mapping using a pixel‐based linear discriminant analysis method that could be applied across Arctic mountains. We trained our classifier using landforms within the Adventdalen catchment in Svalbard and applied it to two adjacent catchments and one in Alaska. Slope gradient, elevation–relief ratio and landscape roughness distinguish landforms to a first order with >80% accuracy. Our simple classification system has a similar overall accuracy when compared across our field sites. The simplicity and robustness of our classification suggest that it is possible to use it to understand the distribution of Arctic mountain landforms using extant digital topography data and without specialized classifications. Our preliminary assessments of the distribution of geomorphic processes within these catchments demonstrate the importance of post‐glacial hillslope processes in governing sediment movement in Arctic mountains

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

Using remote sensing to explore the spectral and spatial characteristics of wetland vegetation.

Wetlands play an important role as ecotones between terrestrial and aquatic habitats and, as a result, represent an environment of high biodiversity and important hydrological function. Ecological understanding in these environments is hampered by difficult terrain and the dynamic and heterogeneous nature of the vegetation. Remote sensing can provide large amounts of contemporaneous data quickly, objectively and over large areas. This study utilises remote sensing data in conjunction with field data and habitat maps derived from traditional ecological surveys to investigate the use of remote sensing as a tool to aid the ecological understanding and monitoring of wetland environments. This study investigated three main objectives; the first two involved the use of field spectrometry from six habitat types in a freshwater wetland in the north of Scotland. Multivariate analyses demonstrated the possibility of distinguishing between these habitat types using field spectra alone. Detailed vegetation datasets were also collected and the relationship between these and variation in the associated spectra was investigated. Significant relationships were established between ordination axes and spectral bands in the green and NIR regions of the spectrum. Results also demonstrated the potential for remote sensing data to characterise the nature of habitat boundaries. The third objective involved the use of airborne imagery to classify remote sensing data into ecologically meaningful classes. Classification accuracies of over 70% were obtained. Work over the last decade has seen a bridging of the relationship between remote sensing and ecology although it is widely acknowledged that our ecological understanding of the remote sensing-vegetation relationship is still limited at many scales and in many ecosystems, not least the wetland environment. This study provides a much needed basis to research in this cross-disciplinary field and identifies further areas that would benefit from future work

Spectral-Spatial Analysis of Remote Sensing Data: An Image Model and A Procedural Design

The distinguishing property of remotely sensed data is the multivariate information coupled with a two-dimensional pictorial representation amenable to visual interpretation. The contribution of this work is the design and implementation of various schemes that exploit this property. This dissertation comprises two distinct parts. The essence of Part One is the algebraic solution for the partition function of a high-order lattice model of a two dimensional binary particle system. The contribution of Part Two is the development of a procedural framework to guide multispectral image analysis. The characterization of binary (black and white) images with little semantic content is discussed in Part One. Measures of certain observable properties of binary images are proposed. A lattice model is introduced, the solution to which yields functional mappings from the model parameters to the measurements on the image. Simulation of the model is explained, as is its usage in the design of Bayesian priors to bias classification analysis of spectral data. The implication of such a bias is that spatially adjacent remote sensing data are identified as belonging to the same class with a high likelihood. Experiments illustrating the benefit of using the model in multispectral image analysis are also discussed. The second part of this dissertation presents a procedural schema for remote sensing data analysis. It is believed that the data crucial to a succc~ssful analysis is provided by the human, as an interpretation of the image representation of the remote sensing spectral data. Subsequently, emphasis is laid on the design of an intelligent implementation of existing algorithms, rather than the development of new algorithms for analysis. The development introduces hyperspectral analysis as a problem requiring multi-source data fusion and presents a process model to guide the design of a solution. Part Two concludes with an illustration of the schema as used in the classification analysis of a given hyperspectral data set

Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994–2022)

[EN] Invasive plants are a serious problem in island ecosystems and are the main cause of the extinction of endemic species. Cuba is located within one of the hotspots of global biodiversity, which, coupled with high endemism and the impacts caused by various disturbances, makes it a region particularly sensitive to potential damage by invasive plants like Dichrostachys cinerea (L.) Wight & Arn. (marabú). However, there is a lack of timely information for monitoring this species, as well as about the land use and land cover (LULC) classes most significantly impacted by this invasion in the last few decades and their spatial distribution. The main objective of this study, carried out in Central Cuba, was to detect and monitor the spread of marabú over a 28-year period. The land covers for the years 1994 and 2022 were classified using Landsat 5 TM and 8 OLI images with three different classification algorithms: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). The results obtained showed that RF outperformed the other classifiers, achieving AUC values of 0.92 for 1994 and 0.97 for 2022. It was confirmed that the area covered by marabú increased by 29,555 ha, from 61,977.59 ha in 1994 to 91,533.47 ha in 2022 (by around 48%), affecting key land covers like woodlands, mangroves, and rainfed croplands. These changes in the area covered by marabú were associated, principally, with changes in land uses and tenure and not with other factors, such as rainfall or relief in the province. The use of other free multispectral imagery, such as Sentinel 2 data, with higher temporal and spatial resolution, could further refine the model’s accuracy.S

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p