Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection
Remote sensing image change detection (CD) is essential for analyzing land surface changes over time, with a significant challenge being the differentiation of actual changes from complex scenes while filtering out pseudo-changes. A primary contributor to this challenge is the intra-class dynamic changes due to phenological characteristics in natural areas. To overcome this, we introduce the InPhea model, which integrates phenological features into a remote sensing image CD framework. The model features a detector with a differential attention module for improved feature representation of change information, coupled with high-resolution feature extraction and spatial pyramid blocks to enhance performance. Additionally, a constrainer with four constraint modules and a multi-stage contrastive learning approach is employed to aid in the model's understanding of phenological characteristics. Experiments on the HRSCD, SECD, and PSCD-Wuhan datasets reveal that InPhea outperforms other models, confirming its effectiveness in addressing phenological pseudo-changes and its overall model superiority.
Code (0)
등록된 구현이 없습니다.
Tasks
Change DetectionContrastive LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Crop Mapping
Accurate crop mapping fundamentally relies on modeling multi-scale spatiotemporal patterns, where spatial scales range from individual field textures to landscape-level context, and temporal scales capture both short-ter…
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, c…
Data Augmentationimage-classificationImage ClassificationMulti-Label Image Classification+1Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches
Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic chara…
Deep LearningPredictionA novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Ea…
Earth Observationfeature selectionUniTS: Unified Spatio-Temporal Generative Model for Remote Sensing
One of the primary objectives of satellite remote sensing is to capture the complex dynamics of the Earth environment, which encompasses tasks such as reconstructing continuous cloud-free image sequences, detecting land …
Time Series ForecastingChange DetectionCloud Removal