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The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

Paper

Introduction

A remote sensing semantic change detection model, Comprehensively leveraged sEmantics and chAnge Relationships Semantics Change Detection model, named ClearSCD.

This new method draws inspiration from the mutual reinforcement of semantic and change information in the multi-task learning model.

Overview of the ClearSCD.

Innovations

The main innovations in ClearSCD are as follows:

  1. We introduced a supervised Semantics Augmented Contrastive Learning (SACL) module, utilizing both local and global data features, along with cross-temporal differences.

  2. A Bi-temporal Semantic Correlation Capture (BSCC) mechanism is designed, allowing for the refinement of semantics through the output of the Binary Change Detection (BCD) branch.

  3. A deep CVAPS module in classification posterior probability space is developed to execute BCD by integrating semantics posterior probabilities instead of high-dimensional features.

Requirements

  1. The pytorch version of torchvision>=0.13.1 is recommended to ensure that the torchvision library contains Efficientnet's pretrained weights.
  2. Then pip install segmentation-models-pytorch to install a Python library Segmentation Models Pytorch for image segmentation based on PyTorch.

Getting Started

  1. Download Hi-UCD series dataset.

  2. Deal with the dataset using clip_image.py, deal_hiucd.py, and write_path.py from the folder scripts.<br> Note: After running the deal_hiucd.py, the classification codes in Hi-UCD with the land cover class in order minus 1, the unlabeled region as 9 in bi-temporal semantic maps, and unlabeled as 255 in BCD.

  3. Run main.py, then you will find the checkpoints in the results folder.

  4. Run test.py, then you will obtain the test metric and visual results. Our checkpoint on the Hi-UCD-mini dataset can be downloaded from Google Drive

Citation

If you use the ClearSCD codes or the LsSCD dataset, please cite our paper:

@article{TANG2024299,
title = {The ClearSCD model: Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery},
author = {Kai Tang and Fei Xu and Xuehong Chen and Qi Dong and Yuheng Yuan and Jin Chen},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {211},
pages = {299-317},
year = {2024},
issn = {0924-2716},
}

Future

We will publish a large-scale semantic change detection (LsSCD) dataset, which consists of Google Earth images from September 2013 and August 2015, with a spatial resolution of 0.6 m and a full size of 48000 × 32500 pixels.

LsSCD reveals urban and rural land cover changes in the city of Nanjing, the capital of Jiangsu Province, China.

Seven LULC types, including building, road, water, bare land, tree, cropland, and others, were recorded in LsSCD.

LsSCD download link (comming soon)

Overview of the LsSCD.