Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data

Farmland parcel-based crop classification using satellite data plays an important role in precision agriculture. In this study, a deep-learning-based time-series analysis method employing optical images and synthetic aperture radar (SAR) data is presented for crop classification for cloudy and rainy regions. Central to this method is the spatial-temporal incorporation of high-resolution optical images and multi-temporal SAR data and deep-learning-based time-series analysis. First, a precise farmland parcel map is delineated from high-resolution optical images. Second, pre-processed SAR intensity images are overlaid onto the parcel map to construct time series of crop growth for each parcel. Third, a deep-learning-based (using the long short-term memory, LSTM, network) classifier is employed to learn time-series features of crops and to classify parcels to produce a final classification map. The method was applied to two datasets of high-resolution ZY-3 images and multi-temporal Sentinel-1A SAR data to classify crop types in Hunan and Guizhou of China. The classification results, with an 5.0% improvement in overall accuracy compared to those of traditional methods, illustrate the effectiveness of the proposed framework for parcel-based crop classification for southern China. A further analysis of the relationship between crop calendars and change patterns of time-series intensity indicates that the LSTM model could learn and extract useful features for time-series crop classification

An Albumin-Binding PSMA Ligand with Higher Tumor Accumulation for PET Imaging of Prostate Cancer

Prostate-specific membrane antigen (PSMA) is an ideal target for the diagnosis and treatment of prostate cancer. Due to the short half-life in blood, small molecules/peptides are rapidly cleared by the circulatory system. Prolonging the half-life of PSMA probes has been considered as an effective strategy to improve the tumor detection. Herein, we reported a 64Cu-labeled PSMA tracer conjugating with maleimidopropionic acid (MPA), 64Cu-PSMA-CM, which showed an excellent ability to detect PSMA-overexpressing tumors in delayed time. Cell experiments in PSMA-positive 22Rv1 cells, human serum albumin binding affinity, and micro-PET imaging studies in 22Rv1 model were performed to investigate the albumin binding capacity and PSMA specificity. Comparisons with 64Cu-PSMA-BCH were performed to explore the influence of MPA on the biological properties. 64Cu-PSMA-CM could be quickly prepared within 30 min. The uptake of 64Cu-PSMA-CM in 22Rv1 cells increased over time and it could bind to HSA with a high protein binding ratio (67.8 ± 1.5%). When compared to 64Cu-PSMA-BCH, 64Cu-PSMA-CM demonstrated higher and prolonged accumulation in 22Rv1 tumors, contributing to high tumor-to-organ ratios. These results showed that 64Cu-PSMA-CM was PSMA specific with a higher tumor uptake, which demonstrated that MPA is an optional strategy for improving the radioactivity concentration in PSMA-expressing tumors and for developing the ligands for PSMA radioligand therapy

Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks

Time-series reconstruction for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and temporal change analysis. In this study, we proposed a superpixel-based prediction transformation-fusion (SPTF) time-series reconstruction method for cloud/shadow-covered optical images. Central to this approach is the incorporation between intrinsic tendency from multi-temporal optical images and sequential transformation information from synthetic aperture radar (SAR) data, through autoencoder networks (AE). First, a modified superpixel algorithm was applied on multi-temporal optical images with their manually delineated cloud/shadow masks to generate superpixels. Second, multi-temporal optical images and SAR data were overlaid onto superpixels to produce superpixel-wise time-series curves with missing values. Third, these superpixel-wise time series were clustered by an AE-LSTM (long short-term memory) unsupervised method into multiple clusters (searching similar superpixels). Four, for each superpixel-wise cluster, a prediction-transformation-based reconstruction model was established to restore missing values in optical time series. Finally, reconstructed data were merged with cloud-free regions to produce cloud-free time-series images. The proposed method was verified on two datasets of multi-temporal cloud/shadow-covered Landsat OLI images and Sentinel-1A SAR data. The reconstruction results, showing an improvement of greater than 20% in normalized mean square error compared to three state-of-the-art methods (including a spatially and temporally weighted regression method, a spectral–temporal patch-based method, and a patch-based contextualized AE method), demonstrated the effectiveness of the proposed method in time-series reconstruction for multi-temporal optical images

Optimization of the photovoltaic performance of ZnO-based organic-inorganic hybrid solar cells

Organic-inorganic hybrid solar cell is a new type of solar cell, with its organic polymers to provide electrons and inorganic semiconductors to accept electrons. The commonly used inorganic semiconductors are nano-sized zinc oxide (ZnO), titanium dioxide (TiO2), cadmium sulfide (CdS), etc. There are many problems needing to be solved in the research process of hybrid solar cells, such as the poor electron transport efficiency, low utilization of solar energy, chemical incompatibility between inorganic semiconductors and organic polymers, and the consequently caused low photoelectric conversion efficiency. Around the above issues, concerning the solar cell with the ZnO semiconductors as electron acceptor, the photovoltaic performance optimization methods are discussed from the aspects of electron acceptors, electron donor materials and the addition of modified layers for the ZnO-based solar cell, and the future development tendency of the hybrid solar cells is also prospected. The optimization of cell performance has brought hope to the low cost and high efficiency application of this hybrid solar cell

For-backward LSTM-based missing data reconstruction for time-series Landsat images

Reconstructing the missing data for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and multi-temporal analysis. In this study, we proposed a deep-learning-based method for cloud/shadow-covered missing data reconstruction for time-series Landsat images. Central to this method is the combined use of autoencoder, long-short-term memory (AE-LSTM)-based similar pixel clustering and for backward LSTM-based time-series prediction. First, manually delineated cloud/shadow-covered masks were overlaid onto multi-temporal satellite images to produce pixel-wise time-series data with masking values. Second, these pixel-wise time series were clustered by an AE-LSTM-based unsupervised method into multiple clusters, for searching similar pixels. Third, for each cluster of target images, a for-backward-LSTM-based model was established to restore missing values in time series data. Finally, reconstructed data were merged with cloud-free (image) regions to produce cloud-free time-series images. The proposed method was applied onto three datasets of multi-temporal Landsat-8 OLI images to restore cloud/shadow-covered images. The reconstruction results, showing an improvement of greater than 10% in normalized mean-square error compared to the state-of-the-art methods, demonstrated the effectiveness of the proposed method in time-series reconstruction for satellite images

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery