Pilot investigation of remote sensing for intertidal oyster mapping in coastal South Carolina: a methods comparison

South Carolina’s oyster reefs are a major component of the coastal landscape. Eastern oysters Crassostrea virginica are an important economic resource to the state and serve many essential functions in the environment, including water filtration, creek bank stabilization and habitat for other plants and animals. Effective conservation and management of oyster reefs is dependent on an understanding of their abundance, distribution, condition, and change over time. In South Carolina, over 95% of the state’s oyster habitat is intertidal. The current intertidal oyster reef database for South Carolina was developed by field assessment over several years. This database was completed in the early 1980s and is in need of an update to assess resource/habitat status and trends across the state. Anthropogenic factors such as coastal development and associated waterway usage (e.g., boat wakes) are suspected of significantly altering the extent and health of the state’s oyster resources. In 2002 the NOAA Coastal Services Center’s (Center) Coastal Remote Sensing Program (CRS) worked with the Marine Resources Division of the South Carolina Department of Natural Resources (SCDNR) to develop methods for mapping intertidal oyster reefs along the South Carolina coast using remote sensing technology. The objective of this project was to provide SCDNR with potential methodologies and approaches for assessing oyster resources in a more efficiently than could be accomplished through field digitizing. The project focused on the utility of high-resolution aerial imagery and on documenting the effectiveness of various analysis techniques for accomplishing the update. (PDF contains 32 pages

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Segmentation and Classification Models Validation Area Mapping of Peat Lands as Initial Value of Fuzzy Kohonen Clustering Network

Ogan Komering Ilir (OKI) is located at the eastern of South Sumatra Province, 2030'-4015' latitude and 104020'-106000' longitude. Digital image of land was captured from Landsat 8 satellite path 124/row 062. Landsat 8 is new generation satellite which has two sensors, Operation Land Manager (OLI) and Thermal Infra-Red Sensor (TIRS). In pre-processing step, there are a geometric correction, radiometric correction, and cropping of the digital images which resulting coordinated geography. Classification uses maximum likelihood estimator algorithm. In segmentation process and classification, grey value spread is into evenly after applying histogram technique. The results of entropy value are7.42 which is the highest of result image classification, then the smallest entropy value in the result of correction mapping are 6.39. The three of them prove that they have enough high entropy value. Then the result of peatlands classification is given overall accuracy value = = 94.0012% and overall kappa value = 0.9230 so the result of classification can be considered to be right

Improved support vector clustering algorithm for color image segmentation

Color image segmentation has attracted more and more attention in various application fields during the past few years. Essentially speaking, color image segmentation problem is a process of clustering according to the color of pixels. But, traditional clustering methods do not scale well with the number of training sample, which limits the ability of handling massive data effectively. With the utilization of an improved approximate Minimum Enclosing Ball algorithm, this article develops an fast support vector clustering algorithm for computing the different clusters of given color images in kernel-introduced space to segment the color images. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to other popular algorithms, it has the competitive performances both on training time and accuracy. Color image segmentation experiments on both synthetic and real-world data sets demonstrate the validity of the proposed algorithm