Wireless aquatic navigator for detection and analysis (WANDA)

The cost of monitoring and detecting pollutants in natural waters is of major concern. Current and forthcoming bodies of legislation will continue to drive demand for spatial and selective monitoring of our environment, as the focus increasingly moves towards effective enforcement of legislation through detection of events, and unambiguous identification of perpetrators. However, these monitoring demands are not being met due to the infrastructure and maintenance costs of conventional sensing models. Advanced autonomous platforms capable of performing complex analytical measurements at remote locations still require individual power, wireless communication, processor and electronic transducer units, along with regular maintenance visits. Hence the cost base for these systems is prohibitively high, and the spatial density and frequency of measurements are insufficient to meet requirements. In this paper we present a more cost effective approach for water quality monitoring using a low cost mobile sensing/communications platform together with very low cost stand-alone ‘satellite’ indicator stations that have an integrated colorimetric sensing material. The mobile platform is equipped with a wireless video camera that is used to interrogate each station to harvest information about the water quality. In simulation experiments, the first cycle of measurements is carried out to identify a ‘normal’ condition followed by a second cycle during which the platform successfully detected and communicated the presence of a chemical contaminant that had been localised at one of the satellite stations

Low-Rank and Sparse Decomposition for Hyperspectral Image Enhancement and Clustering

In this dissertation, some new algorithms are developed for hyperspectral imaging analysis enhancement. Tensor data format is applied in hyperspectral dataset sparse and low-rank decomposition, which could enhance the classification and detection performance. And multi-view learning technique is applied in hyperspectral imaging clustering. Furthermore, kernel version of multi-view learning technique has been proposed, which could improve clustering performance. Most of low-rank and sparse decomposition algorithms are based on matrix data format for HSI analysis. As HSI contains high spectral dimensions, tensor based extended low-rank and sparse decomposition (TELRSD) is proposed in this dissertation for better performance of HSI classification with low-rank tensor part, and HSI detection with sparse tensor part. With this tensor based method, HSI is processed in 3D data format, and information between spectral bands and pixels maintain integrated during decomposition process. This proposed algorithm is compared with other state-of-art methods. And the experiment results show that TELRSD has the best performance among all those comparison algorithms. HSI clustering is an unsupervised task, which aims to group pixels into different groups without labeled information. Low-rank sparse subspace clustering (LRSSC) is the most popular algorithms for this clustering task. The spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC) algorithms is proposed in this dissertation, which extended LRSSC with multi-view learning technique. In this algorithm, spectral and spatial views are created to generate multi-view dataset of HSI, where spectral partition, morphological component analysis (MCA) and principle component analysis (PCA) are applied to create others views. Furthermore, kernel version of SSMLC (k-SSMLC) also has been investigated. The performance of SSMLC and k-SSMLC are compared with sparse subspace clustering (SSC), low-rank sparse subspace clustering (LRSSC), and spectral-spatial sparse subspace clustering (S4C). It has shown that SSMLC could improve the performance of LRSSC, and k-SSMLC has the best performance. The spectral clustering has been proved that it equivalent to non-negative matrix factorization (NMF) problem. In this case, NMF could be applied to the clustering problem. In order to include local and nonlinear features in data source, orthogonal NMF (ONMF), graph-regularized NMF (GNMF) and kernel NMF (k-NMF) has been proposed for better clustering performance. The non-linear orthogonal graph NMF combine both kernel, orthogonal and graph constraints in NMF (k-OGNMF), which push up the clustering performance further. In the HSI domain, kernel multi-view based orthogonal graph NMF (k-MOGNMF) is applied for subspace clustering, where k-OGNMF is extended with multi-view algorithm, and it has better performance and computation efficiency

A Review Paper Based on Content-Based Image Retrieval

The quantity and complexity of digital image data is rapidly expanding. The user does not meet the demands of traditional information recovery technology, so an efficient system for content-based image collection must be developed. The image recovery from material becomes a source of reliable and rapid recovery. In this paper, characteristics such as color correlogram, texture, form, edge density are compared. For understanding and acquiring much better knowledge on a specific subject, literature surveys are most relevant. In this paper, we discuss some technical aspects of the current image recovery systems based on content

The population structure of roundnose grenadier (Coryphaenoides rupestris) in southwestern Norway

Ocean circulation, bathymetric barriers, and ecological processes can hinder the dispersal of marine fishes and thus generate sub-populations. The present study investigated the population structure of a benthopelagic fish, Coryphaenoides rupestris, from three Norwegian fjords and two coastal sites using eight microsatellite DNA markers. Genetic analyses revealed significant population genetic structure across the study area (FST = 0.0297, P < 0.001) and temporal stability in the Skagerrak. There was evidence of highly isolated sub-populations, as shown by significant pairwise differences in tests of genic differentiation and analysis of molecular variance (AMOVA), high inbreeding coefficients (FIS), high homozygosity, and low genetic diversity. Small-scale, within-fjord population structuring was also found in Lustrafjord. Mantel tests revealed a strong effect of isolation by distance and isolation by depth (bottom depth) and a possible effect of bottom temperature. Significant differences in fish condition were found between sites and included length-weight relationships (Analysis of Covariance (ANCOVA): F = 8.249, df = 7, P < 0.001), Hepatosomatic Index (HSI; GLM: F = 252.48, df = 3, P < 0.001) and Gonadosomatic Index (GSI; GLM: F = 15.91, df = 3, P < 0.001). In conclusion, population structuring in C. rupestris along the Norwegian coast seems to be influenced by distance, bathymetric barriers like bottom depth and fjord sills, and differences in fish condition indicate possible differences in environmental conditions between sites. Coryphaenoides rupestris is an overfished species that has been redlisted as critically endangered. Based on the present findings, stock management should consider each of the sub-populations independently, and not depend on recovery through recruitment from neighbouring sub-populations.Master's Thesis in BiologyBIO39

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends

Aspects of reproduction in pink dentex Dentex gibbosus (Rafinesque, 1810) from the Archipelago of Madeira in the northeast Atlantic

This work describes and identifies the macroscopic, and corresponding microscopic, changes of gonads through the annual reproductive cycle of pink dentex, Dentex gibbosus, from the Madeira Archipelago. This new contribution focused on validating a macroscopic maturity scale for this species using a histological technique. A total of 906 individuals were collected from waters around the Madeira Archipelago between September 1997 and December 2008. A six-stage maturity scale based on macroscopic characteristics was used to classify the gonads. The overall ratio of males to females was 1:1.12. The annual gonad development, together with the analysis of monthly indices (gonadosomatic and hepatoso-matic) and complementary histological observations allowed us to conclude that spawning takes place during the summer months, with a peak in May-June

Analytical methods fort he study of color in digital images

La descripció qualitativa dels colors que composen una imatge digital és una tasca molt senzilla pel sistema visual humà. Per un ordinador aquesta tasca involucra una gran quantitat de qüestions i de dades que la converteixen en una operació de gran complexitat. En aquesta tesi desenvolupam un mètode automàtic per a la construcció d’una paleta de colors d’una imatge digital, intentant respondre a les diferents qüestions que se’ns plantegen quan treballam amb colors a dins el món computacional. El desenvolupament d’aquest mètode suposa l’obtenció d’un algorisme automàtic de segmentació d’histogrames, el qual és construït en detall a la tesi i diferents aplicacions del mateix son donades. Finalment, també s’explica el funcionament de CProcess, un ‘software’ amigable desenvolupat per a la fàcil comprensió del color

Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification.

Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images. Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison. This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems. The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks. These features are learned on a per-image basis, so they tend to not generalize well across other datasets. In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification. ``Self-taught\u27\u27 feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification. Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery. Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction). Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user