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.

A Singlestage Multiclass Object Detection Method for Remote Sensing Images Impressive progresses have been achieved in object detection for images by convolution neural networks. However, a robust multiclass object detection method is still one of the great challenges for remote sensing images. Due to the great diversity of scale, orientation, density and background of objects, most advanced object detection algorithms in natural scenes usually suffer a sharp decline in remote sensing images detection. To solve these problems, we proposed a Singlestage Multiclass Object Detection (SMOD) method, aiming at remote sensing images, which can be trained from scratch and detect multiclass objects quickly and precisely. The proposed method introduces a novel Feature Reuse and Attention (FRA) structure as a key module of feature extraction backbone, which combines SE Attention module and dense Feature Reuse connection. Especially, a multiclass detection structure is proposed to learn from multiscale, multilevel feature map and get effective attention representation for multiclass remote sensing object detection. SMOD can be trained from scratch without pretrained network stably and converge well simply by employing batch normalization throughout the network. Experiments show that our trainingfromscratch method can obtain better performance compared with some stateofart algorithms on public multiclass remote sensing dataset AIIA20186.