The unavoidable nature of Ulva prolifera mixed pixel in low-resolution remote sensing images would result in rough boundary of U. prolifera patches, omission of tiny patches, and overestimation of coverage area. The decomposition of U. prolifera mixed pixel addresses the issue of coverage area overestimation, and the remaining problems can be alleviated by subpixel mapping (SPM). Due to the drift and dissipation of U. prolifera , a suitable SPM method is the single image-based unsupervised method. However, the method has difficulties in detail reconstruction, insufficient learning of spectral information, and SPM error introduced by abundance deviation. Therefore, we proposed a multiple-feature decision fusion SPM (MFDFSPM) method. It involves three branches to obtain the spatial, abundance, and spectral features of U. prolifera while considers multi-feature information using the fusion strategy. Experiments on the Geostationary Ocean Color Imager images in the Yellow Sea of China indicate that the MFDFSPM overperforms several typical U. prolifera SPM methods in higher accuracy and stronger robustness in both SPM and abundance calculation, which produced subpixel map with more detailed spatial information and less noise.

针对MODIS(moderate resolution imaging spectroradiometer,中分辨率成像光谱仪)影像浒苔提取中存在的植被指数自适应阈值不普适等问题,本文提出了多指数决策融合的MODIS浒苔提取算法(multi-index decision fusion,MIDF).计算影像的归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI)、差值植被指数(difference vegetation index,DVI)和比值植被指数(ratio vegetation index,RVI),采用局部阈值法对4个指数灰度图进行自适应阈值分割得到初始浒苔覆盖范围,通过"绝对多数投票法"对上述初始浒苔覆盖范围投票表决得到最终提取结果.实验结果表明,MIDF可适应不同密度的浒苔覆盖区域,且精度优于传统的NDVI、EVI、DVI和RVI自适应阈值法.本算法为无监督法,自动化程度较高,可应用于浒苔灾害遥感业务化监测.

Remote sensing technology is widely used for the dynamic monitoring of Enteromorpha prolifera (EP) blooms due to its high temporal resolution and large scale monitoring. Recently, deep learning(DL) methods have been applied to EP analysis due to their excellent feature representation. However, EP information extraction methods based on DL from low-spatial-resolution satellite images are still immature. The main problems with such methods include the insufficiency of spectral and spatial feature learning in low-resolution satellite images, as well as the sample imbalance that DL-based neural networks face in EP information extraction. To solve the above problems, a neural network-based EP extraction method considering sample balance is proposed in this article and named EP rough-then-accurate extraction network. The method consists of two components: EP rough extraction, a strategy that attends to sample balance, and EP accurate extraction, a deep neural network based on one-dimensional convolutional neural network and bidirectional long short-term memory (Bi-LSTM), which fully considers the learned spectral information of each pixel and interpixel contextual dependencies. Geostationary Ocean Color Imager images with 500-m resolution were applied as the LR images in the experiments. The experimental results show that the proposed method has the capability to enhance adaptability in areas with different EP densities (achieving stable and excellent performance) and exhibits at least a 10% gain in F1-score and at least a 6% gain in IoU in extracting EP coverage information over other representative and traditional EP extraction methods in the Yellow Sea region.

Cloud interference often occurs in Enteromorpha prolifera (EP) extraction from MODIS images, with the purpose of solving this problem, a pseudo-invariant feature-based relative radiometric correction algorithm was proposed in this paper for cloud detection, and named PIF-RAC. The pseudo- invariant feature pixels were carried out to find the linear relationship of reflectance between target image and reference image in this algorithm. Then, the cloud detection threshold of the target image was corrected by the above established linear relationship and manual cloud detection threshold of the reference image. The experimental results show the automatic cloud detection effect of the proposed algorithm is close to that of the artificial threshold algorithm, which enables to effectively eliminate different kind of cloud interference for EP information from MODIS images. The PIF-RAC is an unsupervised algorithm with a high level of automation, which can be applied on EP disasters remote sensing operational monitoring.