Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease.

• Double Generative Adversarial Networks for Image Enhancement and Super Resolution. • Improving the accuracy of LPR systems using image super resolution.

Crop type identification is the initial stage and an important part of the agricultural monitoring system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a reliable data source for crop type identification. However, a single-temporal SAR image does not contain enough features, and the unique physical characteristics of radar images are relatively lacking, which limits its potential in crop mapping. In addition, current methods may not be applicable for time-series SAR data. To address the above issues, a new crop type identification method was proposed. Specifically, a farmland mask was firstly generated by the object Markov random field (OMRF) model to remove the interference of non-farmland factors. Then, the features of the standard backscatter coefficient, Sigma-naught (σ₀), and the normalized backscatter coefficient by the incident angle, Gamma-naught (γ₀), were extracted for each type of crop, and the optimal feature combination was found from time-series SAR images by means of Jeffries-Matusita (J-M) distance analysis. Finally, to make efficient utilization of optimal multi-temporal feature combination, a new network, the convolutional-autoencoder neural network (C-AENN), was developed for the crop type identification task. In order to prove the effectiveness of the method, several classical machine learning methods such as support vector machine (SVM), random forest (RF), etc., and deep learning methods such as one dimensional convolutional neural network (1D-CNN) and stacked auto-encoder (SAE), etc., were used for comparison. In terms of quantitative assessment, the proposed method achieved the highest accuracy, with a macro-F1 score of 0.9825, an overall accuracy (OA) score of 0.9794, and a Kappa coefficient (Kappa) score of 0.9705. In terms of qualitative assessment, four typical regions were chosen for intuitive comparison with the sample maps, and the identification result covering the study area was compared with a contemporaneous optical image, which indicated the high accuracy of the proposed method. In short, this study enables the effective identification of crop types, which demonstrates the importance of multi-temporal radar images in feature combination and the necessity of deep learning networks to extract complex features.

A new ground-based interferometric synthetic aperture radar has been developed by iRadar Sdn Bhd, in collaboration with the Centre for Remote Sensing and Surveillance Technologies, Multimedia University, Malaysia. This ground-based radar operates at Ku-band, single VV-polarization, with range resolution up to 0.175 m and azimuth resolution of 5.81 mrad at 1 km. Indoor test and outdoor field measurements have been conducted to verify the performance and specifications of the developed ground-based synthetic aperture radar (GBSAR). This system can be deployed for surface deformation monitoring especially at the high-risk slope for landslide monitoring and detection. This article presents the basic theory of the GBSAR system, the hardware description, as well as some indoor and outdoor field measurement results of the developed system.

Typically, hyperspectral image suffers from redundant information, data scarcity, and class imbalance problems. This letter proposes a hyperspectral image classification framework named as Two-Stream Residual Separable Convolution (2SRS) network that aims to mitigate these problems. Principal Component Analysis (PCA) is first employed to reduce the spectral dimension of the hyperspectral image. Subsequently, the data scarcity and class imbalance problems are overcome via spatial and spectral data augmentations. A novel spectral data creation from image patches is proposed. The augmented samples are fed into the proposed 2SRS network for hyperspectral image classification. We evaluated the proposed method on three benchmark datasets, namely (1) Indian Pines, (2) Pavia University, and (3) Salinas Scene. The proposed method achieved state-of-the-art performance in terms of Overall Accuracy (OA), Average Accuracy (AA), and Kappa Coefficient (Kappa) for both 30% and 10% training set ratio.