# Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/1706.04719 · HTML: https://mlsanity.com/paper/1706.04719/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2017.

## Built on by (most cited first, 10 of 10)

- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images](https://mlsanity.com/paper/2106.12413/) (2021)
- [Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion](https://mlsanity.com/paper/2410.05624/) (2024)
- [Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images](https://mlsanity.com/paper/2103.07935/) (2021)
- [Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models](https://mlsanity.com/paper/2301.01918/) (2023)
- [Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction](https://mlsanity.com/paper/2410.19256/) (2024)
- [Quantitative Assessment of DESIS Hyperspectral Data for Plant Biodiversity Estimation in Australia](https://mlsanity.com/paper/2207.02482/) (2022)
- [Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers](https://mlsanity.com/paper/2206.09731/) (2022)
- [Decadal analysis of sea surface temperature patterns, climatology, and anomalies in temperate coastal waters with Landsat-8 TIRS observations](https://mlsanity.com/paper/2503.05843/) (2025)
- [A Multi-parameter Updating Fourier Online Gradient Descent Algorithm for Large-scale Nonlinear Classification](https://mlsanity.com/paper/2203.08349/) (2022)

## Abstract (arXiv)

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.