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Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks. [ABSTRACT FROM AUTHOR]

In recent years, the conversion of saline-alkali land to rice fields has become the most dominant land use change feature in western Jilin, leading to significant surface greening. Saline–alkali land and paddy fields have distinct surface biophysical properties; however, there is a lack of systematic assessment of the moderating effect of planting rice on saline–alkali land on regional climate by changing surface properties. In this paper, multiscale data on the surface temperature of saline–alkali land and paddy fields were obtained using 1 km MODIS product, 30 m Landsat 8 satellite imagery and centimeter-scale UAV imagery in Da'an City, western Jilin as the study area, and the various characteristics of the surface temperature of saline-alkali land and paddy fields in different months of the year and at different times of the day were analyzed. Furthermore, the effect of rice cultivation in saline–alkali land on the local surface temperature was assessed using a space-for-time approach. The results based on satellite observations including both MODIS and Landsat showed that the surface temperature of saline–alkali land was significantly higher than that of paddy fields during the crop growing season, especially in July and August. The high temporal resolution MODIS LST data also indicated the paddy fields cool the daytime surface temperature, while warming the nighttime surface temperature, which was in contrast for saline–alkali land during the growing season. High-resolution UAV observations in July confirmed that the cooling effect of paddy fields was most significant at the middle of day. From the biophysical perspective, the reclamation of saline–alkali land into paddy fields leads to an increase in leaf area index, followed by a significant increase in evapotranspiration. Meanwhile, rice cultivation in saline–alkali land reduces surface albedo and increases surface net radiation. The trade-off relationship between the two determines the seasonal difference in the surface temperature response of saline–alkali land for rice cultivation. At the same time, the daily cycle of crop evapotranspiration and the thermal insulation effect of paddy fields at night are the main reasons for the intraday difference in surface temperature between saline–alkali land and paddy field. Based on the multiscale assessment of the impact of rice cultivation in saline-alkali land on surface temperature, this study provides a scientific basis for predicting future regional climate change and comprehensively understanding the ecological and environmental benefits of saline–alkali land development. [ABSTRACT FROM AUTHOR]

The optical remote sensing images not only have to make a fundamental tradeoff between the spatial and spectral resolutions, but also are inevitable to be polluted by the clouds; however, the existing pansharpening methods mainly focus on the resolution enhancement of the optical remote sensing images without cloud contamination. How to fuse the cloud-contaminated images to achieve the joint resolution enhancement and cloud removal is a promising and challenging work. In this paper, a pansharpening method for the challenging cloud-contaminated very high-resolution remote sensing images is proposed. Furthermore, the cloud-contaminated conditions for the practical observations with all the thick clouds, the thin clouds, the haze, and the cloud shadows are comprehensively considered. In the proposed methods, a two-step fusion framework based on multisource and multitemporal observations is presented: 1) the thin clouds, the haze, and the light cloud shadows are proposed to be first jointly removed and 2) a variational-based integrated fusion model is then proposed to achieve the joint resolution enhancement and missing information reconstruction for the thick clouds and dark cloud shadows. Through the proposed fusion method, a promising cloud-free fused image with both high spatial and high spectral resolutions can be obtained. To comprehensively test and verify the proposed method, the experiments were implemented based on both the cloud-free and cloud-contaminated images, and a number of different remote sensing satellites including the IKONOS, the QuickBird, the Jilin (JL)-1, and the Deimos-2 images were utilized. The experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For very-high-resolution (VHR) remote sensing images with complex objects and rich textural information, multi-difference image fusion has been proven as an effective method to improve the performance of change detection. However, errors are superimposed during this process and a single spectral feature cannot fully utilize the correlation between pixels, resulting in low robustness. To overcome these problems and optimize the performance of multi-difference image fusion in change detection, we propose a novel multi-difference image fusion change detection method based on a visual attention model (VA-MDCD). First, we construct difference images using change vector analysis (CVA) and spectral gradient difference (SGD). Second, we use the visual attention model to calculate multiple color, intensity and orientation features of the difference images to obtain the difference saliency images. Third, we use the wavelet transform fusion algorithm to fuse two saliency images. Finally, we execute the OTSU threshold segmentation algorithm (OTSU) to obtain the final change detection map. To validate the effectiveness of VA-MDCD on VHR images, two datasets of Jilin 1 and Beijing 2 are selected for experiments. Compared with classical methods, the proposed method has a better performance with fewer missed alarms (MA) and false alarms (FA), which proves that the method has a strong robustness and generalization ability. The F-measure of the two datasets is 0.6671 and 0.7313, respectively. In addition, the results of ablation experiments confirm that the three feature extraction modules of the model all play a positive role. [ABSTRACT FROM AUTHOR]

Studying land use change and its associated climate effects is important to understand the role of human activities in the regulation of climate systems. By coupling remote sensing measurements with a high-resolution regional climate model, this study evaluated the land surface changes and corresponding climate impact caused by planting rice on saline-alkali land in western Jilin (China). Our results showed that paddy field expansion became the dominant land use change in western Jilin from 2015 to 2019, 25% of which was converted from saline-alkali land; this percentage is expected to increase in the near future. We found that saline-alkali land reclamation to paddy fields significantly increased the leaf area index (LAI), particularly in July and August, whereas it decreased albedo, mainly in May and June. Our simulation results showed that planting rice on saline-alkali land can help decrease the air temperature and increase the relative humidity. The temperature and humidity effects showed different magnitudes during the growing season and were most significant in July and August, followed by September and June. The nonradiative process, rather than the radiative process, played a dominant role in regulating the regional climate in this case, and the biophysical competition between evapotranspiration (ET) and albedo determined the temperature and relative humidity response differences during the growing season. [ABSTRACT FROM AUTHOR]

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification. [ABSTRACT FROM AUTHOR]