Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network

Abstract

Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN's shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance.

arXiv commentaccepted by ISPRS Journal of Photogrammetry and Remote Senseing 2017

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
• How transferable are features in deep neural networks?
• Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
• Fully Convolutional Networks for Semantic Segmentation
• Multi-Scale Context Aggregation by Dilated Convolutions
• A Survey on Object Detection in Optical Remote Sensing Images
• ParseNet: Looking Wider to See Better
• Object Detectors Emerge in Deep Scene CNNs
• Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
• Pyramid Scene Parsing Network
• Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
• Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
• Learning to Refine Object Segments

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• A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
• Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
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