Interactive Multiscale Attention Fusion with Sparse Feature Convolution Network for Satellite Image Change Detection

Summary

This study presents an Interactive Multiscale Attention fusion model with Sparse Feature Convolution Networks to improve satellite image change detection. The approach takes advantage of multiscale feature extraction, the selective focus on attention, and sparse convolutions to effectively detect and localize change in bi-temporal satellite images by reducing computational complexity.

Abstract

Change detection using bi-temporal satellite imagery is an important problem in earth monitoring that aims at identifying and localizing surface changes between a temporal acquisition and distinguishing actual pseudo-changes due to seasonal cycles, atmospheric factors, or man-made developments. Current deep learning methods typically face a one-way bias in their temporal modeling, which restricts the usability of large-scale spatial relationships to empower the proper characterization of change patterns. Although recent transformer-based techniques are highly accurate, their computational requirements are also quite large, thus limiting their use on resource-constrained tasks. In response to these limitations, an Interactive Multiscale Attention Fusion (IMAF) network with Sparse Feature Convolution (SFC) based on Ghost modules to attain effective multi-scale features extraction is proposed in this paper. The network utilizes advanced encoder-decoder architecture supplemented with interactive attention modules in different scales. The network works with complementary forward and backward attention streams: the former one records progressive temporal variation and the latter one verifies a consistency of the changes by reverse temporal analysis. This interactive method allows effective representations of the duplex model of temporal relationships without being computationally prohibitive, enhancing far better approaches to distinguish legitimate land cover moves, versus noise absent noise-induced variation. Two benchmark datasets are experimented with: the Onera Satellite Change Detection dataset (OSCD), and the SZTAKI AirChange datasets. The significant experiments made on OSCD datasets reveal that proposed approach attain competitive F1 rates of 58.14% (13 channels) and 50.68% (3 channels) with just 2.13M parameters and 19.6G FLOPS, 19 times few parameters, and 5.6x inference turnover as compared to ChangeFormer. Competitive performance on Szada/1 and Tiszadob/3 test set samples of the SZTAKI dataset, F1-score of 74.29% and 92.86% respectively makes it possible to implement in the most edge devices, real-time analytics, and extensive mapping systems where operational energy directly depends on computational energy.

Introduction

Unprecedented levels of urbanization in the global environment, environmental resource loss, and alterations of the landscape due to climate have directly triggered the necessity to provide an exact, timely and automatic system of monitoring that can identify and measure changes over the surface of the Earth. Satellite remote sensing has grown to be the basic building block of mass environmental surveillance since they provide consistent temporal and spatial coverage in addition to being vital in the complete examination of the change. Bi-temporal Satellite imagery change detection is the characterization of surface changes between two sequential acquisitions of the s....

Protocol

1. Ethics statement

1. Both Onera Satellite Change Detection (OSCD) and SZTAKI AirChange datasets used in this study consist of publicly available RGB and multispectral images covering diverse geographical characteristics. These datasets do not contain any restricted or sensitive data. Therefore, no ethical consent is required for this work.

2. Materials and equipment

1. Perform testing on a Windows 11 computer containing an Intel Core i7 10870H processor, an NVIDIA GeForce GTX 1650 Ti chip with 4GB of VRAM, and 32GB of RAM.
   NOTE: The programming environment is Python 3.8, and ....

Results

The proposed change detection method was evaluated against established baseline approaches using two publicly available benchmark datasets to assess its performance across different spectral resolutions and environmental conditions. The experimental results reveal significant improvements in balancing detection accuracy with false positive control, particularly demonstrating the method's ability to maintain high recall rates while substantially enhancing precision compared to traditional approaches.

Discussion

This study presents an interactive multi-scale attention fusion-based change detection network for detecting changes in bi-temporal satellite images. The integration of a bi-directional attention mechanism, unlike unidirectional methods, facilitates a complete cross-temporal feature-to-feature interaction with complementary forward and backward attention signals. Progressive temporal changes of the forward attention stream are captured by the attention difference i.e. older to newer temporal acquisitions, whereas the cha.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CUDA Toolkit	NVIDIA Corporation	Version 11.3	GPU acceleration for deep learning computations
cuDNN	NVIDIA Corporation	Version 8.2	Optimized GPU deep learning library
Intel  processor	Intel Corporation	Core i7 10870H	Computational platform used for training and evaluating the proposed deep learning model
NumPy	Open Source	Version 1.21.0	Numerical array processing
NVIDIA GeForce	NVIDIA Corporation	GTX 1650 Ti (4 GB VRAM)	Graphics processing unit used for accelerated model training
Onera Satellite Change Detection (OSCD) Dataset	ONERA	Publicly available optical RGB and Multispectral image dataset used for training and evaluation.	https://rcdaudt.github.io/oscd/
Python	Python Software Foundation	Version 3.8	Programming language used for algorithm implementation
PyTorch	Meta AI (Open Source)	Version 1.12.1	Open-source deep learning framework used to develop and train the proposed model
Scikit-learn	Open Source	Version 1.0.2	Performance evaluation metrics
SZTAKI AirChange Benchmark Dataset	SZTAKI	Publicly available optical RGB aerial image dataset used for training and evaluation.	http://web.eee.sztaki.hu/remotesensing/airchange_benchmark.html
Torchvision	Meta AI (Open Source)	Version 0.13.1	Dataset loading and image transformations
Windows OS	Microsoft	11	Operating System