Deep learning

Aerospace engineering

Pattern recognition (psychology)

Feature (linguistics)

Intersection (aeronautics)

Politics

Discriminative model

Data mining

Engineering

Philosophy

Political science

Artificial intelligence

Set (abstract data type)

Programming language

Change detection

Object detection

Representation (politics)

Computer science

Linguistics

Feature extraction

Convolutional neural network

Segmentation

IEEE Geoscience and Remote Sensing Letters

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual-task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three subnetworks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building data set show that the proposed method is effective for building change detection and achieves state-of-the-art performance in terms of four metrics on the WHU building data set: precision, recall, F1-score, and intersection over union.

Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model