UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation

Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at \url{https://github.com/zhu-xlab/UCDFormer}Comment: 16 pages, 7 figures, IEEE Transactions on Geoscience and Remote Sensin

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster’s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.publishedVersio

Landslide Detection Using a Saliency Feature Enhancement Technique from LiDAR-Derived DEM and Orthophotos

© 2013 IEEE. This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not require landslide inventory data for training purposes. It comprises several steps. First, it enhances potential landslide pixels. Then, it removes the image background using slope information derived from a very high-resolution LiDAR-based (light detection and ranging) digital elevation model (DEM). After that, morphological analysis was applied to remove small objects, separate landslide objects from each other, and fill the gaps between large bare soil objects and urban objects. Finally, landslide scars were detected using the Fuzzy C-means (FCM) clustering algorithm. The proposed method was developed based on datasets acquired over the Kinta Valley area in Malaysia and tested on another area with a different environment and topography (i.e., Cameron Highlands). The results showed that the proposed landslide detection technique could detect landslides in the training area with a Prediction Accuracy, Kappa index, and Mean Intersection-Over-Union (mIOU) of 71.12%, 0.81, and 68.52%, respectively. The Prediction Accuracy, Kappa index, and mIOU of the method based on the test dataset were 65.78%, 0.68, and 56.14%, respectively. These results show that the proposed method can be used for landslide inventory mapping and risk assessments

Earth resources: A continuing bibliography with indexes

This bibliography lists 579 reports, articles, and other documents introduced into the NASA scientific and technical information system. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Achieving sustainable development goals coupling earth observation data with machine learning

Tese de Doutoramento em Engenharia e Gestão Industrial, Universidade Lusíada, Vila Nova de Famalicão, 2021Exame público realizado em 09 de Junho de 2022The main purpose of this work is to assess and understand the achievement of Sustainable Development Goals by means of Earth Observation (EO) data and Machine Learning (ML) technologies. Thus, this study intends to promote and suggest the use of EO and ML in benefits to the Sustainable Development Goals (SDGs) to support and optimize the actual industry and field processes and moreover provide new insights (techniques) on EO approaches and applicability as well as ML techniques. A review on the Sustainable Development concept and its goals is presented followed by EO data and methods and its approaches relevant to this field, giving special attention to the contribution of ML methods and algorithms as well as their potential and capabilities to support the achievement of SDGs. Additionally, different ML approaches and techniques are reviewed (i.e., Classification and Regression techniques, Non-Binary Decision Tree (NBDT), and two novel methods are proposed, designated as: Random Forest built based on the Non-Binary Decision Tree (NBRF) and Fusion of techniques). Both developed methods are applied, optimized and validated to two case studies also aligned with specific SGDs: Case study I – Identification and mapping of healthy or infected crops, tackling SDGs 2, 8, 9 and 12; and Case study II - Deep-sea mining exploitation SDGs 8, 9, 12 and 14). Such is achieved by using data provided by European satellites or programs that allows to also contribute to the goals for Europe’s Space strategy. For Case study I, the parameters analysed to achieve the respective SDGs correspond to: several vegetation indices as well as the values of the spectral bands. Such parameters have been extracted by means of EO data (from Sentinel-2) and validated with different ML approaches. The results obtained from the different ML approaches suggest that for Case study I, the best classification technique (overall accuracy of 92.87%) as well as the best regression (Root mean square error of 0.148) corresponds to the Fusion of techniques All the applied techniques, however, show their applicability on this case study with good results, disregarding the NBDT which is the “weakest” one (best result on all tests: accuracy of 57.07%). For Case study II, the parameters analysed to achieve the respective SDGs correspond to the topography of the seafloor and, physical and biogeochemical ocean’s parameters. Such parameters have been extracted by means of EO data (from CMEMS and GEBCO) and validated with different ML approaches. The results of these approaches suggests that the best technique corresponds to the Fusion of techniques with a root mean square error of 0.196. However, not all the techniques proved to be appropriated, where the NBDT present the worst results (best result on all tests: accuracy 60.62%). Overall, it is observed that EO plays a key role in the monitoring and achievement of the SDGs given its cost-effectiveness pertaining to data acquisition on all scales and information richness, and the success of ML upon EO data analysis. The applicability of ML techniques allied to EO data has proven, by both case studies, that can contribute to the SDGs and can be extrapolated to other applications and fields. Keywords: Sustainable Development Goals; Earth Observation; Europe Space Strategy; Machine Learning; Deep-sea Mining; Agriculture