Deepwater Horizon MC252 response data from the Environmental Response Management Application (ERMA) containing Texture Classifying Neural Network Algorithm (TCNNA) from Synthetic Aperture Radar (SAR) nearshore potential oiling footprints collected from 2010-04-29 to 2010-08-11 in the Northern Gulf of Mexico (NCEI Accession 0163819)

This archival information package (AIP) contains Environmental Response Management Application (ERMA) GIS layers of outputs from Synthetic Aperture Radar (SAR) imagery that has been processed using the Texture Classifying Neural Network Algorithm (TCNNA). This algorithm classifies SAR data on a pixel by pixel basis, into oil or not-oil classes. The fully implemented TCNNA routine produces approximately a one megabyte georectified, raster image in which all pixels receive a binary classification of 1 for no oil and 0 for oil. Each classified raster image was converted to polygons and clipped to within approximately 20 kilometers of the coastline. This data was collected from April 29th 2010 to August 11th 2010. These data were collected during the response to the Mississippi Canyon 252 Deepwater Horizon oil spill in the Northern Gulf of Mexico and used as part of the Programmatic Damage Assessment and Restoration Plan (PDARP).

本档案信息包（AIP）包含环境响应管理应用（ERMA）的地理信息系统（GIS）图层，这些图层是通过纹理分类神经网络算法（TCNNA）处理合成孔径雷达（SAR）影像得到的输出结果。该算法以逐像素方式将SAR数据分类为油类或非油类。完全实现的TCNNA程序可生成约1兆字节的地理校正栅格图像，其中所有像素均采用二分类标记——1代表非油，0代表油。每个分类后的栅格图像均被转换为多边形，并裁剪至海岸线约20公里范围内。本数据的收集时间为2010年4月29日至8月11日。这些数据是在应对墨西哥湾北部密西西比峡谷252号深水地平线漏油事件期间收集的，并被用作规划性损害评估与恢复计划（PDARP）的一部分。