Multisource Remote Sensing Data Classification Based on Convolutional Neural Network

By Xiaodong Xu, Wei Li, Qiong Ran, Qian Du, Lianru Gao, Bing Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2018

614 citations recorded by the source corpus.

Subfields: Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/4397dfccb9649f0dfbfce7090f5b801fce24a72e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Shutao Li
2. Qian Du
3. Danfeng Hong
4. Xudong Kang
5. Pedram Ghamisi
6. Jing Yao
7. Bing Zhang
8. Jocelyn Chanussot

In Media Technology

1. Shutao Li
2. Jocelyn Chanussot
3. Leyuan Fang
4. Danfeng Hong
5. Pedram Ghamisi
6. Jon Atli Benediktsson
7. Xudong Kang
8. Yushi Chen

Citations in the corpus

• Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “For instance, in [107] and [108], a deep CNN with two-branch architecture was proposed to extract the joint spectral–spatial features from HSIs.”
• A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, methodology): “These methods [66, 67, 68] combine HSIs and LiDAR data together to increase the discriminability of sample features.”
• HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers (2020, background): “HYPERSPECTRAL imagery (HSI) incorporates hundreds of narrow contiguous spectral bands carrying abundant spectral information [1], [2], thus enabling accurate pixel-level”
• More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (2020, background): “In [24], authors put forward to use the two-branch CNNs with cascade blocks for automatic feature extraction and fusion of multisource RS data.”
• Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, background): “Many scholars have introduced deep learning technology to remote sensing processing and have achieved excellent performance in image classification [25], [26], semantic segmentation [27]–[29], object detection [30], [31], change detection [17], [32]–[35], and other tasks.”
• Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, methodology): “In [108] and [114], a parallel structure was employed to extract spectral-spatial features.”
• Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, methodology): “Using parallel convolutional streams, References [165,166] fuse LiDAR and MSI/HSI data at the feature engineering stage for the crop and land cover classification.”