No abstract

One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared images improved slightly the accuracy over the 2015 image. Even though classifications using hyperspectral data cubes of 64 bands resulted in relatively larger accuracies than with 16 bands, classification error matrices were not statistically different. Alternative imaging platforms (like an unmanned aerial vehicle and a Cessna 172 aircraft) and settings of the flights were discussed using simulated imaging projects assuming the same study area and field of application. Ultra-light aircraft-based hyperspectral and colour-infrared imaging was considered to be a technically and economically sound solution for urban green space inventories to facilitate tree mapping, characterization, and monitoring.

No abstract

ABSTRACT:Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in this study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related to class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The classification accuracy is improved up to 5%, with comparison to the best single source classification accuracy.

No abstract

Fast urbanization requires complex management of green spaces inside districts and all around the cities. In this context, the use of high-resolution imagery could give a fast overview of species distribution in the considered study zone, and could even permit species recognition by taking advantage of high spectral resolution (i.e. superspectral/hyperspectral imagery). In this study, we aim to explore the feasibility of eight vegetation species recognition inside Kaunas city (Lithuania). The goal is to determine the potential of metric/centimetric spatial resolution imagery with less than hundred bands and a limited spectral interval (e.g. Vis-NIR), to be able to recognize urban vegetation species. The ground truth samples were also limited for some of the considered species. The method included pre-treatments based on vegetation masking and feature selection using Minimum Noise Fraction (MNF). Support Vector Machine (based classifier) showed encouraging performance over Spectral Angle Mapper (SAM), the accuracies were not notably high in term of statistical analysis (i.e. up to 46% of overall accuracy) but the visual inspection showed coherent distribution of the detected species.

Objects recognition in urban environment using multiband imagery, is a difficult process, implying the use of elaborated and complex image processing methods, which are used to enhance the detection efficiency. The urban mosaics are characterized by multiple materials (e.g. manmade, urban vegetation, bare soil, transport infrastructure, etc.), which are combined together to form a complex patchwork. This study aim to take advantage of the multiband imagery, to assess the feasibility degree of the urban objects detection, and to explore some of the applications related to the multiband hyperspectral imagery classification. AbstractUrban objects classification by spectral library: feasibility and applications JURSE 2017, DUBAI, 06-08 Mars 2017 1. Hyperspectral data and spectral library acquisition 2. Method Spectral variability Urban objects recognitionAn original technique based on the urban objects identification by spectral library, using airborne hyperspectral imagery, is presented in this study. The potential to distinguish some of the common roofing's materials in the city of Kaunas was demonstrated. Nevertheless, some issues need to be addressed in the future, as the high interclass and intraclass correlations regarding some urban materials, leading to misclassifications. Asbestos type roofing's and bitumen roofing's were difficult to identify and presents high correlation with the other materials like asphalt roads and pavements. To improve the spectral classification results, we proposed a morphological rectification based on attributes filtering, and rules construction, the rectification seemed suitable, and permits to eliminate the misclassified pixels (i.e. artefacts, roads, shadows, etc.). For the future, we are planning to 1) enrich the spectral library with more materials, 2) test other datasets including more bands, and 3) use other classifiers (e.g. SVM, neural networks).

Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences the accuracy of urban object detection, and (2) the urban structure, which influences the morphological recognition and characterization of built-up areas. In this study, high repetitiveness remote sensing Sentinel-2A and SPOT 6 high-resolution satellite images were combined to characterize and detect urban built-up areas over the city of Yakutsk. High temporal resolution of Sentinel-2A allows land use change detection and metric spatial resolution of SPOT 6 allows the characterization of built-up areas’ socioeconomic functions and uses.