An EndtoEnd OilSpill Monitoring Method for Multisensory Satellite Images Based on Deep Semantic Segmentation In remotesensing images, a detected oilspill area is usually affected by spot noise and uneven intensity, which leads to poor segmentation of the oilspill area. This paper introduced a deep semantic segmentation method that combined a deepconvolution neural network with the fully connected conditional random field to form an endtoend connection. On the basis of Resnet, it first roughly segmented a multisource remotesensing image as input by the deep convolutional neural network. Then, we used the Gaussian pairwise method and meanfield approximation. The conditional random field was established as the output of the recurrent neural network. The oilspill area on the sea surface was monitored by the multisource remotesensing image and was estimated by optical image. We experimentally compared the proposed method with other models on the dataset established by the multisensory satellite image. Results showed that the method improved classification accuracy and captured fine details of the oilspill area. The mean intersection over the union was 82.1%, and the monitoring effect was obviously improved.

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for MultiSource and MultiTemporal High Spatial Resolution Remote Sensing Imagery High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic objectbased image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottomup classifications from visual features to semantic categories, but ignore topdown feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent xe2x80x9cselflearning abilityxe2x80x9d of deep learning to construct a modified structure of the Mask RCNN method which integrates bottomup and topdown processes for water recognition. Compared with traditional methods, our method is completely datadriven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multisource and multitemporal water bodies, and can effectively avoid confusion with shadows and other ground features.