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

arXiv: https://arxiv.org/abs/1703.06452 · HTML: https://mlsanity.com/paper/1703.06452/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2017.

## Built on by (most cited first, 30 of 37)

- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks](https://mlsanity.com/paper/2309.16499/) (2023)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Geographical Knowledge-driven Representation Learning for Remote Sensing Images](https://mlsanity.com/paper/2107.05276/) (2021)
- [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://mlsanity.com/paper/2102.02531/) (2021)
- [SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation](https://mlsanity.com/paper/2404.02638/) (2024)
- [SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentation](https://mlsanity.com/paper/2405.01992/) (2024)
- [Semantic Segmentation with Active Semi-Supervised Learning](https://mlsanity.com/paper/2203.10730/) (2022)
- [Distilling Inter-Class Distance for Semantic Segmentation](https://mlsanity.com/paper/2205.03650/) (2022)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review](https://mlsanity.com/paper/2304.13880/) (2023)
- [Oriented Bounding Boxes for Small and Freely Rotated Objects](https://mlsanity.com/paper/2104.11854/) (2021)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation](https://mlsanity.com/paper/2305.12691/) (2023)
- [An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2105.04132/) (2021)
- [Enhancing Environmental Monitoring through Multispectral Imaging: The WasteMS Dataset for Semantic Segmentation of Lakeside Waste](https://mlsanity.com/paper/2407.17028/) (2024)
- [OSTA: One-shot Task-adaptive Channel Selection for Semantic Segmentation of Multichannel Images](https://mlsanity.com/paper/2305.04766/) (2023)
- [Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer](https://mlsanity.com/paper/2410.01092/) (2024)
- [Designing a Classifier for Active Fire Detection from Multispectral Satellite Imagery Using Neural Architecture Search](https://mlsanity.com/paper/2410.05425/) (2024)
- [Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images](https://mlsanity.com/paper/2203.02820/) (2022)
- [Exploration of Convolutional Neural Network Architectures for Large Region Map Automation](https://mlsanity.com/paper/2211.03854/) (2022)
- [BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation](https://mlsanity.com/paper/2401.04330/) (2024)
- [C3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories](https://mlsanity.com/paper/2407.04100/) (2024)
- [ClassWise-CRF: Category-Specific Fusion for Enhanced Semantic Segmentation of Remote Sensing Imagery](https://mlsanity.com/paper/2504.21491/) (2025)
- [SILOP: An Automated Framework for Semantic Segmentation Using Image Labels Based on Object Perimeters](https://mlsanity.com/paper/2303.07892/) (2023)
- [Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data](https://mlsanity.com/paper/2310.07223/) (2023)
- [Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning](https://mlsanity.com/paper/2311.11827/) (2023)

## Abstract (arXiv)

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.