Looking for change? Roll the Dice and demand Attention

TL;DR

This paper introduces a novel deep learning framework for semantic change detection in high-resolution aerial images, featuring new loss functions, attention modules, and architecture, achieving state-of-the-art results on benchmark datasets.

Contribution

It presents a new architecture with innovative attention modules, loss functions, and feature extraction units specifically designed for semantic change detection.

Findings

Achieved state-of-the-art F1 and IoU scores on LEVIRCD and WHU datasets.

Validated new feature extraction blocks on CIFAR10.

Demonstrated effectiveness of the proposed architecture in change detection tasks.

Abstract

Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a reliable deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, new attention modules, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity, that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new spatial and channel convolution Attention layer (the FracTAL). The new attention layer, designed specifically for vision tasks, is memory efficient, thus suitable for use in all levels of deep convolutional networks. Based on these, we introduce two new efficient self-contained feature extraction convolution units. We validate the performance of these feature extraction building blocks on the CIFAR10 reference data and compare the results with standard ResNet modules. Further, we introduce a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. Our network moves away from any notion of subtraction of feature layers for identifying change. We validate our approach by showing excellent performance and achieving state of the art score (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets.