4.7 Article

A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction

Related references

Note: Only part of the references are listed.
Article Environmental Sciences

Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series

Marcel Schwieder et al.

Summary: Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. This study used Sentinel-2 and Landsat 8 data to develop an algorithm for detecting mowing events in Germany from 2017 to 2020. The results showed that the detection of mowing events was influenced by data availability and regional variations, but the approach was still able to map the intensity of grassland management throughout large areas and identify regional differences.

REMOTE SENSING OF ENVIRONMENT (2022)

Proceedings Paper Computer Science, Artificial Intelligence

MAXIM: Multi-Axis MLP for Image Processing

Zhengzhong Tu et al.

Summary: Recent progress on Transformers and multilayer perceptron (MLP) models has led to new network architectural designs for computer vision tasks. This work presents MAXIM, a multi-axis MLP based architecture that serves as an efficient and flexible general-purpose vision backbone for image processing tasks. MAXIM achieves state-of-the-art performance on various image processing tasks, requiring fewer parameters and FLOPs than competing models.

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022) (2022)

Article Geography, Physical

Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences

Jorge Andres Chamorro Martinez et al.

Summary: This paper introduces convolutional recurrent networks for crop recognition in tropical regions with complex spatiotemporal dynamics, achieving per-date crop classification. Experimental results show that the proposed architectures outperform state-of-the-art methods based on recurrent networks in terms of accuracy and F1 score in tropical regions.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2021)

Article Environmental Sciences

An SVM-Based Nested Sliding Window Approach for Spectral-Spatial Classification of Hyperspectral Images

Jiansi Ren et al.

Summary: This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors to extract spatial information from hyperspectral images (HSI) and reconstruct original data. The NSW-PCA-SVM model combines NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM) to achieve high classification accuracy, with the advantage of easily adjustable parameters for better performance.

REMOTE SENSING (2021)

Article Environmental Sciences

Towards interpreting multi-temporal deep learning models in crop mapping

Jinfan Xu et al.

Summary: Multi-temporal deep learning methods show promising performance in large-scale crop mapping by transforming remote sensing data into high-dimensional features for crop identification. The study demonstrates the importance of complete time series input, key growth periods, and critical bands for crop discrimination. By analyzing input feature importance and hidden features, deep learning models extract refined information for effective crop classification.

REMOTE SENSING OF ENVIRONMENT (2021)

Article Environmental Sciences

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Chong Luo et al.

Summary: This study evaluated the feasibility of object-oriented crop classification using Sentinel-1 images in GEE. The results showed that crop classification accuracy was higher with shorter time intervals of composite images.

REMOTE SENSING (2021)

Article Agriculture, Multidisciplinary

A new attention-based CNN approach for crop mapping using time series Sentinel-2 images

Yumiao Wang et al.

Summary: The study proposed a novel attention-based convolutional neural network approach (Geo-CBAM-CNN) for crop classification using time series Sentinel-2 images. By integrating geographic information of crops and enhancing the model's attention spectrally and spatially, the Geo-CBAM-CNN model achieved the best performance in large scale applications.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Article Remote Sensing

Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images

Shunping Ji et al.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2020)

Article Environmental Sciences

Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery

Sebastian Preidl et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Environmental Sciences

Deep learning in environmental remote sensing: Achievements and challenges

Qiangqiang Yuan et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Geography, Physical

Self-attention for raw optical Satellite Time Series Classification

Marc Russwurm et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2020)

Article Environmental Sciences

Deep learning based multi-temporal crop classification

Liheng Zhong et al.

REMOTE SENSING OF ENVIRONMENT (2019)

Article Environmental Sciences

Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning

Philippe Rufin et al.

REMOTE SENSING (2019)

Article Computer Science, Information Systems

Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

Angel Garcia-Pedrero et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

Marc Russwurm et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2018)

Article Agriculture, Multidisciplinary

How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?

Hu Qiong et al.

JOURNAL OF INTEGRATIVE AGRICULTURE (2017)

Article Geography, Physical

Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information

Jiahua Zhang et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2014)

Article Geography, Physical

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

Xianfeng Jiao et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2014)

Article Environmental Sciences

Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery

Liheng Zhong et al.

REMOTE SENSING OF ENVIRONMENT (2014)

Article Geography, Physical

Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey

Ugur Alganci et al.

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING (2013)

Article Agriculture, Multidisciplinary

Evaluating high resolution SPOT 5 satellite imagery for crop identification

Chenghai Yang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2011)

Article Multidisciplinary Sciences

Solutions for a cultivated planet

Jonathan A. Foley et al.

NATURE (2011)

Article Geography, Physical

Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

UC Benz et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2004)

Article Environmental Sciences

A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery

J Rogan et al.

REMOTE SENSING OF ENVIRONMENT (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)