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从 Landsat 8 OLI 图像中提取沿海筏式养殖数据。

Extracting Coastal Raft Aquaculture Data from Landsat 8 OLI Imagery.

机构信息

Geological Engineering and Institute of Surveying and Mapping, Chang'an University, Xi'an 710054, China.

State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China.

出版信息

Sensors (Basel). 2019 Mar 11;19(5):1221. doi: 10.3390/s19051221.

DOI:10.3390/s19051221
PMID:30862001
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6427152/
Abstract

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale.

摘要

有关沿海筏式养殖的信息,特别是空间分布数据,对于海洋资源的可持续发展和环境保护至关重要。商业高空间分辨率卫星图像可以准确地定位筏式养殖。然而,使用这种昂贵的图像进行此类分析需要大量的图像。相比之下,中分辨率卫星图像(如 Landsat 8 图像)免费提供,覆盖面积大,数据量少,并且可以提供可接受的结果。因此,我们使用 Landsat 8 图像来提取沿海筏式养殖的存在。由于沿海筏式养殖区域的高叶绿素浓度导致归一化差异植被指数(NDVI)和边缘特征对于水背景非常明显,因此我们将这些特征集成到所提出的方法中。我们使用来自中国东部从北到南的三个地点来验证该方法,并将其与我们之前仅使用基于对象的视觉显着 NDVI(OBVS-NDVI)特征的方法进行比较。新提出的方法不仅保持了 OBVS-NDVI 的真实阳性结果,而且还消除了 OBVS-NDVI 的大多数假阴性结果。因此,该新方法有可能用于大规模快速监测沿海筏式养殖。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/b25eace58271/sensors-19-01221-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/094075f48733/sensors-19-01221-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/1d01a8cb52d7/sensors-19-01221-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/244cde9f8f3f/sensors-19-01221-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/27658bf55fcc/sensors-19-01221-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/2cbec43b2b00/sensors-19-01221-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/01ba6091eda8/sensors-19-01221-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/b25eace58271/sensors-19-01221-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/094075f48733/sensors-19-01221-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/1d01a8cb52d7/sensors-19-01221-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/244cde9f8f3f/sensors-19-01221-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/27658bf55fcc/sensors-19-01221-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/2cbec43b2b00/sensors-19-01221-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/01ba6091eda8/sensors-19-01221-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31eb/6427152/b25eace58271/sensors-19-01221-g007.jpg

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本文引用的文献

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Automated parameterisation for multi-scale image segmentation on multiple layers.用于多层多尺度图像分割的自动参数化
ISPRS J Photogramm Remote Sens. 2014 Feb;88(100):119-127. doi: 10.1016/j.isprsjprs.2013.11.018.
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Geographic Object-Based Image Analysis - Towards a new paradigm.基于地理对象的图像分析——迈向新范式。
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