The spectral signature of vegetation in the image is easily affected by background soil reflectance and spectral variability of vegetation reflectance, spectral variability is one of the major error sources of unmixing. The traditional algorithms do not solve spectral variability problem from the mechanism. In this paper, we take advantage of radiative transfer model, in order to describe the spectral variability of endmember. As a result, the spectral variability can be quantitatively described. The experimental results show that the PROSAIL Model Spectral Unmixing (PMSU) algorithm has higher unmixing precision than the other algorithms.
Retrieving the parameters in water quality with multispectral data using neural network is increasingly popular, however,
the training process with large amount samples and calculation with large-volume data are a time-consuming work.
Many emergency pollution events need quick responses for practical use. In this paper, an improved membrane
computing strategy is presented. This strategy is a hybrid one combining the framework and evolution rules of P systems
with active membranes and neural networks, and it involves a dynamic structure including membrane fusion and
division, which helpful to enhance the information communication and beneficial to reduce the computation. Then, a
parallel implementation with the training result is discussed. Experiments with Landsat datasets to obtain suspended
sediment are carried out to demonstrate the practical capabilities of this introduced strategy.
Spectral unmixing in hyperspectral remote sensing image has been widely researched in the last two decades. N-FINDR
algorithm is one of the most classical and commonly-used endmember extraction algorithms. Nevertheless, it is a timeconsuming
task that cannot meet the time requirement of many applications. In order to make N-FINDR computationally
feasible, we consider parallel implementation of N-FINDR algorithm on hybrid multiple-core CPU and GPU parallel
platform. First, a multi-core CPU-based parallel N-FINDR algorithm is considered based on a modified N-FINDR with
two improvements. And by using the increasing programmability and parallelism of commodity GPU, a GPU-based
parallel N-FINDR is presented. Finally, by taking advantages of the capability of the aforementioned algorithms, a
hybrid multiple-core CPU and GPU parallel N-FINDR is proposed by using a virtual thread technique and an adaptive
algorithm in which the computational load can be adaptively adjusted according to the capability of CPU and GPU. In
experiment, our proposed parallel N-FINDR algorithms improved the accuracy of the original N-FINDR algorithm, and
most importantly, they greatly improved the performance of N-FINDR algorithm.
Noise estimation is an important task in hyperspectral remote sensing image process. However, hyperspectral image has
limitation on spatial resolution. Usually, More than one type of materials are embedded in ground sampling
distance(GSD) of the image. Therefore, this paper focuses on the evaluation on the effect of spatial resolution on noise
estimation. A series of simulative images with different spatial resolution levels are generated for the evaluation. Noise
standard deviation, the normalized eigenvalues of minimum noise fraction (MNF) transformation and the endmember
extraction accuracy are used to evaluate the noise estimation methods. In experiments, the results demonstrate that the
methods who estimate noise in spectral domain have robust stability of spatial resolution and perform better in low
spatial resolution levels.
Pattern Recognition has been successfully applied to target detection. The characteristic of target pattern determines the detection ability in pattern recognition. The pattern of spectral signature at hyperspectral resolution provides more distinguished spectral feature for target detection and then improves the detection ability. However, hyperspectral image has limitation on low resolution in spatial. Therefore, this article focus on analyze on the target detection ability at the sub-pixel scale in different spatial resolution, considering two critical factors, i.e. spatial response in sensor and background interferer. Experiment data is simulated by inducing these two factors. Target-to-Clutter-Ratio (TCR2) curve and Receiver Operating Characteristic (ROC) curve with Uniform Target Detector (UTD) analyze on the simulated data. We conclude that spatial response of the sensor and the background interferer induce uncertainty into target detection ability and usually weakens it. It gives rise to a new requirement for hyperspectral target detection that should be considerate for the effect caused by spatial resolution.