Ground elevation models and land cover classifers for decimetre resolution urban monitoring

This thesis presents methods that enable the generation of quantitative environmental indicators for remotely monitoring urban regions. Its contributions are a new morphological and surface fitting hybrid algorithm for the generation of ground elevation models, a vegetation classifier and significant research into Canonical Variate Analysis with Rational Polynomials (a feature extraction method that normalises the topographic illumination effect). These methods were tested on a 9600 square kilometre, 20cm resolution dataset covering Perth

Monitoring Cloud-prone Complex Landscapes At Multiple Spatial Scales Using Medium And High Resolution Optical Data: A Case Study In Central Africa

Tracking land surface dynamics over cloud-prone areas with complex mountainous terrain and a landscape that is heterogeneous at a scale of approximately 10 m, is an important challenge in the remote sensing of tropical regions in developing nations, due to the small plot sizes. Persistent monitoring of natural resources in these regions at multiple spatial scales requires development of tools to identify emerging land cover transformation due to anthropogenic causes, such as agricultural expansion and climate change. Along with the cloud cover and obstructions by topographic distortions due to steep terrain, there are limitations to the accuracy of monitoring change using available historical satellite imagery, largely due to sparse data access and the lack of high quality ground truth for classifier training. One such complex region is the Lake Kivu region in Central Africa. This work addressed these problems to create an effective process for monitoring the Lake Kivu region located in Central Africa. The Lake Kivu region is a biodiversity hotspot with a complex and heterogeneous landscape and intensive agricultural development, where individual plot sizes are often at the scale of 10m. Procedures were developed that use optical data from satellite and aerial observations at multiple scales to tackle the monitoring challenges. First, a novel processing chain was developed to systematically monitor the spatio-temporal land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification, using the state-of-the-art machine learning classifier Random Forest, was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988-2001 and 2001- 2011 periods was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. While useful on a regional scale, Landsat data can be inadequate for more detailed studies of land cover change. Based on an increasing availability of high resolution imagery and light detection and ranging (LiDAR) data from manned and unmanned aerial platforms (\u3c1m \u3eresolution), a study was performed leading to a novel generic framework for land cover monitoring at fine spatial scales. The approach fuses high spatial resolution aerial imagery and LiDAR data to produce land cover maps with high spatial detail using object-based image analysis techniques. The classification framework was tested for a scene with both natural and cultural features and was found to be more than 90 percent accurate, sufficient for detailed land cover change studies

Shadow compensation from UAV images based on texture-preserving local color transfer

The lack of color information and texture information in the shadow region seriously affect the recognition and interpretation of remote sensing image information. The commonly used methods focus on the restoration of texture information, but it is often easy to overcompensate, resulting in color distortion of the shadow region. However, some methods only ensure accurate correction of color information, and tend to cause texture blurring. In order to not lose the texture information and to accurately compensate the color information in the shadow region of the image, we propose a shadow compensation method from UAV images based on texture-preserving local color transfer in this paper. Firstly, homogeneous regions are extracted from UAV images and homogeneous subregion segmentation is performed on the basis of homogeneous regions using the mean shift method. Secondly, in combination with the shadow mask, each shadow subregion is matched with the corresponding non-shadow subregion based on its texture features and spatial distance. Then, the matched non-shadow subregion is used as the reference region, and the color transfer based on preserving texture is performed on the shadow subregion. Finally, pixel-by-pixel width shadow compensation is applied to the penumbra region. The results of the qualitative and quantitative analysis validate the accuracy and effectiveness of the proposed methodology to compensate for the color and texture details of the shadow regions

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a total of 70 research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the preferable way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Networks (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach

Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic QuickBird images

Multitemporal land-use analysis is becoming increasingly important for the effective management of earth resources. Despite that, consistent differences in the viewing and illumination geometry in satellite-borne imagery introduce some issues in the creation of land-use classification maps. The focus of this study is settlement classification with high-resolution panchromatic acquisitions, using texture features to distinguish between settlement classes. The important multitemporal variance component of shadow is effectively removed before feature determination, which allows for minimum-supervision across-date classification. Shadow detection based on local adaptive thresholding is employed and experimentally shown to outperform existing fixed threshold shadow detectors in increasing settlement classification accuracy. Both same and across-date settlement accuracies are significantly improved with shadow masking during feature calculation. A statistical study was performed and found to support the hypothesis that the increased accuracy is due to shadow masking specifically.National Research Foundationhttp://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859hb2014ai201

Dataset shift in land-use classification for optical remote sensing

Multimodal dataset shifts consisting of both concept and covariate shifts are addressed in this study to improve texture-based land-use classification accuracy for optical panchromatic and multispectral remote sensing. Multitemporal and multisensor variances between train and test data are caused by atmospheric, phenological, sensor, illumination and viewing geometry differences, which cause supervised classification inaccuracies. The first dataset shift reduction strategy involves input modification through shadow removal before feature extraction with gray-level co-occurrence matrix and local binary pattern features. Components of a Rayleigh quotient-based manifold alignment framework is investigated to reduce multimodal dataset shift at the input level of the classifier through unsupervised classification, followed by manifold matching to transfer classification labels by finding across-domain cluster correspondences. The ability of weighted hierarchical agglomerative clustering to partition poorly separated feature spaces is explored and weight-generalized internal validation is used for unsupervised cardinality determination. Manifold matching solves the Hungarian algorithm with a cost matrix featuring geometric similarity measurements that assume the preservation of intrinsic structure across the dataset shift. Local neighborhood geometric co-occurrence frequency information is recovered and a novel integration thereof is shown to improve matching accuracy. A final strategy for addressing multimodal dataset shift is multiscale feature learning, which is used within a convolutional neural network to obtain optimal hierarchical feature representations instead of engineered texture features that may be sub-optimal. Feature learning is shown to produce features that are robust against multimodal acquisition differences in a benchmark land-use classification dataset. A novel multiscale input strategy is proposed for an optimized convolutional neural network that improves classification accuracy to a competitive level for the UC Merced benchmark dataset and outperforms single-scale input methods. All the proposed strategies for addressing multimodal dataset shift in land-use image classification have resulted in significant accuracy improvements for various multitemporal and multimodal datasets.Thesis (PhD)--University of Pretoria, 2016.National Research Foundation (NRF)University of Pretoria (UP)Electrical, Electronic and Computer EngineeringPhDUnrestricte

Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available

Investigating the impact of Tourism on forest cover in the Annapurna conservation area through Remote Sensing and Statistical Analysis

Tourism is Nepal’s largest industry giving people in rural areas an alternative to subsistence farming. Tourism can have an impact on the forest cover of a region as trees provide firewood for cooking and heating and timber for building accommodation. In 1986 the Annapurna conservation area project was started to ensure that tourism was managed sustainably, which includes minimising the impacts on the forest cover. This study assesses the impacts of tourism on the forest cover in the Annapurna region by comparing Landsat images from 1999 and 2011. This was achieved through spectral classification of different landcover and assessing the change in forest cover in relation to increasing distances from tourism villages. A major problem with remote sensing in mountainous regions such as Nepal is shadow caused by the relief. This issue was addressed by only assessing areas which were free from shadow, which in effect meant a sample was used rather than the whole study region. The results indicate that there has been an 8 per cent reduction in overall forest extent, but this change varies by region. In the northern drier regions there has been a net increase in forest cover, while in the southern regions there has been a net reduction in forests. The influence of tourism facilities on forest is also variable. Around each of the sample tourism villages there was a general trend of decreasing removal of forest at greater distances from each village, which indicates tourism does have a negative impact on forests. However, there was an opposite trend in the northern villages that were well inside the conservation area

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi