Hyperspectral remote sensing has been widely used in more and more fields nowadays, such as the oil spill analysis and chlorophyll estimation in green plants. To decompose the mixed pixels people always turns to the traditional method of Least Squares Method now. But its main drawback is that it involves a large amount of matrix operations, especially regarding to the huge dimension of hyperspectral images. So it will take much time. Motivated by this, in this paper we have developed a new model of endmember abundance estimate which is referred to as Spectral Characteristic Based Abundance Estimation Model (SCBAEM). The model is based on the fitted curve in which spectral characteristic were considered. To establish the model, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) were utilized between endmember and mixed pixels. The main contributions of the paper are summarized as follows: Firstly, we build the model by calculating normalized SAM (NSAM) and normalized SID (NSID). Secondly, to test and verify the accuracy of the model, oil slick experiment is carried out. Finally, we further conduct its application in the real hyperspectral oil spill images which is from Peng-lai 19-3C platform. The results of simulation experiments and real hyperspectral image demonstrate that the proposed model could achieve the efficiency of LSM. At the same time, the time cost can be reduced greatly. So it can satisfy the real-time need.
Oil spills could occur in many conditions, which results in pollution of the natural resources, marine environment and economic health of the area. Whenever we need to identify oil spill, confirm the location or get the shape and acreage of oil spill, we have to get the edge information of oil slick images firstly. Hyperspectral remote sensing imaging is now widely used to detect oil spill. Active Contour Models (ACMs) is a widely used image segmentation method that utilizes the geometric information of objects within images. Region based models are less sensitive to noise and give good performance for images with weak edges or without edges. One of the popular Region based ACMs, active contours without edges Models, is implemented by Chan-Vese. The model has the property of global segmentation to segment all the objects within an image irrespective of the initial contour. In this paper, we propose an improved CV model, which can perform well in the oil spill hyper-spectral image segmentation. The energy function embeds spectral and spatial information, introduces the vector edge stopping function, and constructs a novel length term. Results of the improved model on airborne hyperspectral oil spill images show that it improves the ability of distinguishing between oil spills and sea water, as well as the capability of noise reduction.
Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills’ image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators’ result of edge gray level and the image’s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image’s edge detection.
In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.