To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved.

Pests and diseases affect the yield and quality of grapes directly and engender noteworthy economic losses. Diagnosing “lesions” on vines as soon as possible and dynamically monitoring symptoms caused by pests and diseases at a larger scale are essential to pest control. This study has appraised the capabilities of high-resolution unmanned aerial vehicle (UAV) data as an alternative to manual field sampling to obtain sampling canopy sets and to supplement satellite-based monitoring using machine learning models including partial least squared regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme learning regression (ELR) with a new activation function. UAV data were acquired from two flights in Turpan to determine disease severity (DS) and disease incidence (DI) and compared with field visual assessments. The UAV-derived canopy structure including canopy height (CH) and vegetation fraction cover (VFC), as well as satellite-based spectral features calculated from Sentinel-2A/B data were analyzed to evaluate the potential of UAV data to replace manual sampling data and predict DI. It was found that SVR slightly outperformed the other methods with a root mean square error (RMSE) of 1.89%. Moreover, the combination of canopy structure (CS) and vegetation index (VIs) improved prediction accuracy compared with single-type features (RMSEcs of 2.86% and RMSE_VIs of 1.93%). This study tested the ability of UAV sampling to replace manual sampling on a large scale and introduced opportunities and challenges of fusing different features to monitor vineyards using machine learning. Within this framework, disease incidence can be estimated efficiently and accurately for larger area monitoring operation.

Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method

During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing technology independent of light and weather, the increasingly accessible Synthetic Aperture Radar (SAR) is an attractive data for assessing building damage. This paper aims to use Sentinel-1A radar images to rapidly assess seismic damage in the early phases after the disaster. A simple and robust method is used to complete the task of surface displacement analysis and building disaster monitoring. In order to obtain the coseismic deformation field, differential interferometry, filtering and phase unwrapping are performed on images before and after the earthquake. In order to detect the damage area of buildings, the Interferometric Synthetic Aperture Radar (InSAR) and Polarimetric Synthetic Aperture Radar (PolSAR) techniques are used. A simple and fast method combining coherent change detection and polarimetric decomposition is proposed, and the complete workflow is introduced in detail. In our experiment, we compare the detection results with the ground survey data using an unmanned aerial vehicle (UAV) after the earthquake to verify the performance of the proposed method. The results indicate that the experiment can accurately obtain the coseismic deformation field and identify the damaged and undamaged areas of the buildings. The correct identification accuracy of collapsed and severely damaged areas is 86%, and that of slightly damaged and undamaged areas is 84%. Therefore, the proposed method is extremely effective in monitoring seismic-affected areas and immediately assessing post-earthquake building damage. It provides a considerable prospect for the application of SAR technology.

Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively applied in the monitoring of open-pit mines where vegetation is sparse and land cover is dominated by bare rock. The main objective of this study is to monitor the mining activities of four open-pit coal mines in the Wucaiwan mining area in China from 2018 to 2020, namely No. 1, No. 2 (containing two mining areas), and No. 3. We use the normalized differential activity index (NDAI) based on the coherence coefficient as an indicator of the mine activity due to its robustness to temporal and spatial decorrelation. After analyzing and removing the decorrelation caused by rain and snow weather, 70 NDAI images in 12-day intervals are obtained from Sentinel-1A InSAR coherence images. Then, the annually-averaged NDAI images are applied to an RGB composite technique (red for 2018, green for 2019, blue for 2020) to express the interannual variation of the mining activities. Points of interest are then selected for NDAI time series analysis. The RGB composite results indicated that No. 1 and 3 open-pit coal mines were continuously mined during the three years; whereas, the two mining areas of No. 2 were mainly active in 2018. The 12-day NDAI time-series graphs of No. 2 open-pit coal mine also indicate that the coal piles located in the coal transferring area of the first mining area were not completely removed until April 2019. It is also seen that the second mining area was decommissioned in November 2018 and became rehabilitated in July 2019. Results were validated using the Sentinel-2A images and related background information confirming the efficiency of the proposed approach for monitoring the mining activity in open-pit mines.

The Sarez Dam, currently recognized as the world’s highest natural dam, holds immense economic significance, necessitating the reliable monitoring and early detection of potential deformations. This study utilizes the Beidou high-precision deformation monitoring system for the dam. Employing baseline network solutions, precise point positioning, and real-time kinematic positioning, the monitoring data from October 2021 to March 2023 were meticulously processed and comparatively analyzed. The results reveal varied degrees of displacement deformation at all sites, with horizontal deformation towards the lake center and vertical deformation showing subsidence. The three-dimensional vector average displacement is 29.1 mm, with an average monthly rate of 4.3 mm/month, and the maximum deformation is 41.4 mm. The cause of the horizontal deformation towards the lake center may be a slow collapse of the coastal soil and rock towards the lake center. Additionally, the monitoring detected a 7.2-magnitude earthquake on 23 February 2023, 52 km from the dam, causing an average displacement of 22.5 mm towards the south, which is 20 times the monthly deformation rate. In summary, deformation is present within the dam monitoring area, with a relatively stable deformation rate, warranting continued tracking and monitoring. Furthermore, earthquakes significantly impact dam deformation, necessitating heightened attention to the potential for seismic-induced dam damage in the future.