Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning

Abstract

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.

45 pages

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• Remote Sensing Image Scene Classification: Benchmark and State of the Art
• Learning to Segment Object Candidates
• Learning Deconvolution Network for Semantic Segmentation
• Simultaneous Detection and Segmentation
• Synthesizing Training Images for Boosting Human 3D Pose Estimation
• High-Resolution Multispectral Dataset for Semantic Segmentation
• Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
• Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields

Cited by in corpus (26)

• UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
• ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
• MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery
• A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images
• An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
• Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings
• Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
• Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
• Oriented Bounding Boxes for Small and Freely Rotated Objects
• AeroRIT: A New Scene for Hyperspectral Image Analysis
• GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
• Learning CNN filters from user-drawn image markers for coconut-tree image classification
• An informative path planning framework for UAV-based terrain monitoring
• On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
• Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
• Exploration of Convolutional Neural Network Architectures for Large Region Map Automation
• Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data
• BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation
• Geographical Knowledge-driven Representation Learning for Remote Sensing Images
• CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images
• ClassWise-CRF: Category-Specific Fusion for Enhanced Semantic Segmentation of Remote Sensing Imagery
• SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters
• WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming
• ELKPPNet: An Edge-aware Neural Network with Large Kernel Pyramid Pooling for Learning Discriminative Features in Semantic Segmentation
• Active Learning for UAV-based Semantic Mapping
• Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation