The existing salient object detection model can only detect the approximate location of salient object, or highlight the background, to resolve the above problem, a salient object detection method was proposed based on image semantic features. First of all, three novel salient features were presented in this paper, including object edge density feature (EF), object semantic feature based on the convex hull (CF) and object lightness contrast feature (LF). Secondly, the multiple salient features were trained with random detection windows. Thirdly, Naive Bayesian model was used for combine these features for salient detection. The results on public datasets showed that our method performed well, the location of salient object can be fixed and the salient object can be accurately detected and marked by the specific window.
Pixel Purity Index (PPI) is a very popular endmember finding algorithm due to its availability in ENVI software. According to the band sequential (BSQ) format of data acquisition this paper introduces a new concept of executing PPI band-by-band in a progressive manner. It is called progressive band processing of PPI (PBP-PPI) which allows users to process PPI band by band without waiting for full bands of data information acquired. To accomplish this goal PPI must be capable of calculating and updating PPI counts of data samples band by band. Furthermore, progressive-band-processing progressive PPI (PBP-P-PPI) and progressive-band-processing causal PPI (PBP-C-PPI) are proposed to address the issues that the number of skewers is undefined and only partial pixels are available correspondingly. Many benefits can be gained from PBP-PPI, for example, providing progressive profiles of PPI counts of data samples as more bands are included for data processing, finding crucial bands according to progressive changes in PPI counts.
OSP has been used widely in detection and abundance estimation for about twenty years. But it can’t
apply nonnegative and sum-to-one constraints when being used as an abundance estimator. Fully
constrained least square algorithm does this well, but its time cost increases greatly as the number of
endmembers grows. There are some tries for unmixing spectral under fully constraints from different
aspects recently. Here in this paper, a new fully constrained unmixing algorithm is prompted based on
orthogonal projection process, where a nearest projected point is defined onto the simplex constructed
by endmembers. It is much easier, and it is faster than FCLS with the mostly same unmixing results. It
is also compared with other two constrained unmixing algorithms, which shows its effectiveness too.
Using maximal simplex volume as an optimal criterion for finding endmembers is a common approach and has been widely studied in the literature. Interestingly, very little work has been reported on how simplex volume is calculated. It turns out that the issue of calculating simplex volume is much more complicated and involved than what we may think. This paper investigates this issue from two different aspects, geometric structure and eigen-analysis. The geometric structure is derived from its simplex structure whose volume can be calculated by multiplying its base with its height. On the other hand, eigen-analysis takes advantage of the Cayley-Menger determinant to calculate the simplex volume. The major issue of this approach is that when the matrix is ill-rank where determinant is desired. To deal with this problem two methods are generally considered. One is to perform data dimensionality reduction to make the matrix to be of full rank. The drawback of this method is that the original volume has been shrunk and the found volume of a dimensionality-reduced simplex is not the real original simplex volume. Another is to use singular value decomposition (SVD) to find singular values for calculating simplex volume. The dilemma of this method is its instability in numerical calculations. This paper explores all of these three methods in simplex volume calculation. Experimental results show that geometric structure-based method yields the most reliable simplex volume.
Progressive band processing (PBP) processes data band by band according to the Band SeQuential (BSQ) format acquired by a hyperspectral imaging sensor. It can be implemented in real time in the sense that data processing can be performed whenever bands are available without waiting for data completely collected. This is particularly important for satellite communication when data download is limited by bandwidth and transmission. This paper presents a new concept of processing a well-known technique, Orthogonal Subspace Projection (OSP) band by band, to be called PBPOSP. Several benefits can be gained by PBP-OSP. One is band processing capability which allows different receiving ends to process data whenever bands are available. Second, it enables users to identify significant bands during data processing. Third, unlike band selection which requires knowing the number of bands needed to be selected or band prioritization PBP-OSP can process arbitrary bands in real time with no need of such prior knowledge. Most importantly, PBP can locate and identify which bands are significant for data processing in a progressive manner. Such progressive profile resulting from PBP-OSP is the best advantage that PBP-OSP can offer and cannot be accomplished by any other OSP-like operators.
Region growing is one of the popular segmentation algorithms for 2-D image, which comes up a continuous interested region. How to extent this method to hyperspectral image processing effectively is a problem needs to be discussed deeply. Here in this paper, three ways of using region growing in hyperspectral scenario are explored to separate oil from sea water. Furthermore, in order to release the influence of sunlight, a modification to growing rule is prompted, considering the property of local region. At last, a normalized ATGP is used to obtain more potential target. The experiment results show that combining unmixing techniques with region growing is better than other methods.
As the hyperspectral image combines spacial information with spectral information, the spectroscopic data can describe the characteristics of surface feature more accurately, and provide possibilities for classification and quantitative calculation for the surface features. Now, the unmixing technology for mixed pixel has become a hot topic in this field. The technology for pixel unmixing contains two main directions. The first one is based on linear mixing model. This model assumes that the pixel is formed by endmembers according to linear relationship. The methods based on this model are easy to be implemented. But the ideal model can’t describe the mixed relation of the surface features accurately. So the accuracy of abundance estimation can’t be guaranteed. The second one is based on non-linear model. This method could get good analytical results, but they are mainly established for particular surface features and difficult for implementation. This paper was mainly aimed at the research of abundance estimation. A simplified Hapke model is proposed to be applied to actual hyperspectral image of oil spilling, so as to obtain the estimation of oil thickness. The Hapke model could transform the non-linear relationship to linear relationship for hyperspetral data. The spectral reflectances of non-linear relationship are transformed to spectral albedo satisfying linear relationship, without changing the abundance. At last, this model is applied to actual hyperspectral image of oil spilling, achieving estimation for oil thickness.
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.
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.