scholarly journals A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection

2020 ◽  
Vol 12 (10) ◽  
pp. 1662 ◽  
Author(s):  
Hao Chen ◽  
Zhenwei Shi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Large Set ◽  
Temporal Attention ◽  
Computational Overhead ◽  
Image Change Detection ◽  
Image Pairs ◽  
Public Datasets

Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination variations and misregistration errors overwhelm the real object changes. Exploring the relationships among different spatial–temporal pixels may improve the performances of CD methods. In our work, we propose a novel Siamese-based spatial–temporal attention neural network. In contrast to previous methods that separately encode the bitemporal images without referring to any useful spatial–temporal dependency, we design a CD self-attention mechanism to model the spatial–temporal relationships. We integrate a new CD self-attention module in the procedure of feature extraction. Our self-attention module calculates the attention weights between any two pixels at different times and positions and uses them to generate more discriminative features. Considering that the object may have different scales, we partition the image into multi-scale subregions and introduce the self-attention in each subregion. In this way, we could capture spatial–temporal dependencies at various scales, thereby generating better representations to accommodate objects of various sizes. We also introduce a CD dataset LEVIR-CD, which is two orders of magnitude larger than other public datasets of this field. LEVIR-CD consists of a large set of bitemporal Google Earth images, with 637 image pairs (1024 × 1024) and over 31 k independently labeled change instances. Our proposed attention module improves the F1-score of our baseline model from 83.9 to 87.3 with acceptable computational overhead. Experimental results on a public remote sensing image CD dataset show our method outperforms several other state-of-the-art methods.

scholarly journals Change Capsule Network for Optical Remote Sensing Image Change Detection

2021 ◽  
Vol 13 (14) ◽  
pp. 2646
Author(s):  
Quanfu Xu ◽  
Keming Chen ◽  
Guangyao Zhou ◽  
Xian Sun
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Small Data ◽  
Optical Remote Sensing ◽  
Optical Remote Sensing Image ◽  
Vector Difference ◽  
Image Change Detection ◽  
Image Pairs

Change detection based on deep learning has made great progress recently, but there are still some challenges, such as the small data size in open-labeled datasets, the different viewpoints in image pairs, and the poor similarity measures in feature pairs. To alleviate these problems, this paper presents a novel change capsule network by taking advantage of a capsule network that can better deal with the different viewpoints and can achieve satisfactory performance with small training data for optical remote sensing image change detection. First, two identical non-shared weight capsule networks are designed to extract the vector-based features of image pairs. Second, the unchanged region reconstruction module is adopted to keep the feature space of the unchanged region more consistent. Third, vector cosine and vector difference are utilized to compare the vector-based features in a capsule network efficiently, which can enlarge the separability between the changed pixels and the unchanged pixels. Finally, a binary change map can be produced by analyzing both the vector cosine and vector difference. From the unchanged region reconstruction module and the vector cosine and vector difference module, the extracted feature pairs in a change capsule network are more comparable and separable. Moreover, to test the effectiveness of the proposed change capsule network in dealing with the different viewpoints in multi-temporal images, we collect a new change detection dataset from a taken-over Al Udeid Air Basee (AUAB) using Google Earth. The results of the experiments carried out on the AUAB dataset show that a change capsule network can better deal with the different viewpoints and can improve the comparability and separability of feature pairs. Furthermore, a comparison of the experimental results carried out on the AUAB dataset and SZTAKI AirChange Benchmark Set demonstrates the effectiveness and superiority of the proposed method.

scholarly journals Regression Tree CNN for Estimation of Ground Sampling Distance Based on Floating-Point Representation

2019 ◽  
Vol 11 (19) ◽  
pp. 2276
Author(s):  
Jae-Hun Lee ◽  
Sanghoon Sull
Keyword(s):  
Remote Sensing ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Input Image ◽  
Google Earth ◽  
Aerial Image ◽  
Floating Point ◽  
Binomial Tree ◽  
Sampling Distance ◽  
Public Datasets

The estimation of ground sampling distance (GSD) from a remote sensing image enables measurement of the size of an object as well as more accurate segmentation in the image. In this paper, we propose a regression tree convolutional neural network (CNN) for estimating the value of GSD from an input image. The proposed regression tree CNN consists of a feature extraction CNN and a binomial tree layer. The proposed network first extracts features from an input image. Based on the extracted features, it predicts the GSD value that is represented by the floating-point number with the exponent and its mantissa. They are computed by coarse scale classification and finer scale regression, respectively, resulting in improved results. Experimental results with a Google Earth aerial image dataset and a mixed dataset consisting of eight remote sensing image public datasets with different GSDs show that the proposed network reduces the GSD prediction error rate by 25% compared to a baseline network that directly estimates the GSD.

Sign in / Sign up

Export Citation Format

Share Document