Resolution enhancement for drill-core hyperspectral mineral mapping

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling

Fully‐connected semantic segmentation of hyperspectral and LiDAR data

Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once, by considering the effects of the neighbouring pixels. In this study, the authors propose a new semantic segmentation model that fuses hyperspectral images with light detection and ranging (LiDAR) data in the three‐dimensional space defined by Universal Transverse Mercator (UTM) coordinates and solves the task using a fully‐connected conditional random field (CRF). First, the authors’ pairwise energy in the CRF model takes into account the UTM coordinates of the data; and performs fusion in the real world coordinates. Second, as opposed to the commonly used Markov random fields (MRFs) which consider only the nearby pixels; the fully‐connected CRF considers all the pixels in an image to be connected. In doing so, they show that these long‐term interactions significantly enhance the results when compared to traditional MRF models. Third, they propose an adaptive scaling scheme to decide the weights of LiDAR and hyperspectral sensors in shadowy or sunny regions. Experimental results on the Houston dataset indicate the effectiveness of their method in comparison to the several MRF based approaches as well as other competing methods

Improving the performance of construction project using green building principles

Construction projects need to be effectively managed to prevent conflicts and problems between the constructor, users, and the environment, and this requires considering the principles of eco-friendliness, especially for housing projects. This study was, therefore, conducted to set priorities to improve the performance of housing construction project management using the green building principle initiated in 2013 as a benchmark for the Greenship assessment for New Buildings by the Green Building Council of Indonesia. The Importance Performance Analysis was applied to measure the level of user interest and satisfaction on the performance of constructors in constructing housing projects. The results showed technical factors of construction management are dominant in evaluating constructor’s performance. The users expect their houses to be constructed on time according to planned and agreed schedules. Moreover, the factors associated with green building-based performance include reusable and recyclable building materials (mean score of satisfaction level = 3.682 and interest level = 3.882), availability of facilities for people with disabilities (mean score of satisfaction level = 3.420 and interest level = 3.874), eco-friendly materials and construction (mean score of satisfaction level = 3.634 and interest level = 3.840), friendly design and materials for people with disabilities, infants, seniors, and pregnant women (mean score of satisfaction level = 3.704 and interest level = 3.832), and the use of solar energy (mean score of satisfaction level = 3.688 and interest level = 3.825). This, therefore, means the green building is considered important by the users during the construction and implementation processe

ANÁLISE DO NÍVEL DE LEGENDA DE CLASSIFICAÇÃO DE AREAS URBANAS EMPREGANDO IMAGENS MULTIESPECTRAIS E HIPERESPECTRAIS COM OS MÉTODOS ÁRVORE DE DECISÃO C4.5 E FLORESTA RANDÔMICA

Ambientes urbanos representam uma das áreas mais desafiadoras do sensoriamento remoto devido à grande diversidade encontrada nos materiais presentes na sua superfície. O uso de imagens com alta resolução espacial e alta resolução espectral surge como uma alternativa para aplicações urbanas, pois a combinação destas duas características permite uma melhor detecção e discriminação de alvos. O presente trabalho tem um duplo objetivo: i) avaliar dois conjuntos de dados na classificação fina de alvos urbanos para dois níveis de legenda (com 11 e 38 classes de cobertura do solo): um deles composto exclusivamente por uma imagem orbital multiespectral (WV-2) e o outro conjunto composto exclusivamente por uma imagem aerotransportada hiperespectral (SpecTIR), ii) bem como testar o desempenho de dois métodos diferentes de classificação de imagens, Árvore de Decisão C4.5 e Floresta Randômica (Random Forest), para ambos os níveis de legenda. Oito experimentos de classificação foram realizados para atender a tais objetivos de investigar a eficácia dos sensores e dos métodos em dois níveis de detalhamento. Foram obtidas classificações de elevada acurácia. Demonstrou-se para todos os níveis de detalhamento e métodos que as classificações obtidas com dados do sensor SpecTIR apresentaram resultados significantemente superiores aos das classificações com dados do sensor WV-2