Mangrove canopy height information is crucial to carbon stock and biomass analyses. However, estimation of this height is challenging because of large areas involved, and field conditions of mangrove forests. Remote sensing satellite imagery has been used for canopy height mapping because it offers several advantages. This study developed a spatial–spectral–temporal deep learning regression model with convolutional long short-term memory (ConvLSTM) and transformer (hereafter referred to as the SST-CLT model) to map mangrove canopy height over large area. The SST-CLT model consists of two sub-models trained simultaneously. The first sub-model is fusion extractor to extracts spatial–spectral–temporal information from Sentinel-1 time-series data by using a ConvLSTM. It also extracts spatial–spectral information from Sentinel-2 data using a two-dimensional convolutional block. The second sub-model is a regressor contains Swin transformer and final convolutional regression layer. Data from light detection and ranging canopy height model were employed as the target data to train the proposed model. The SST-CLT model was tested on two datasets collected from Florida: large dataset for the Everglades National Park (ENP) and small dataset for the Charlotte Harbor Preserve State Park (CHPSP). The SST-CLT model achieved a mean absolute error (MAE) of 1.924 and 1.913 m for the ENP and CHPSP datasets, respectively. Moreover, it achieved root mean square error (RMSE) values of 2.471 and 2.440 m for these datasets, respectively. The SST-CLT model was compared with that of other regression models. The results indicated that the MAE and RMSE of the proposed SST-CLT were lower than those of the other models. https://github.com/ilhamjamal/SST-CLT.

This paper proposes a novel sequential neural network that integrates the collaborative context-learning module to learn sparse and lengthy POI data for top-N recommendation tasks. Our proposed model explores either time and distance irregularities from check-in sequences and incorporates them into the model learning process by carrying a decomposition of the memory cell into short-and long-term interests. The short-term counterpart is adjusted by a proper weight and combined with the long-term counterpart before further processing with the light version of LSTM, where forget and input gates are united into a single decision unit. Also, we further improve our proposed model by allowing multiple contexts collaboratively trained together under the gating mechanism of recurrent neural networks. Extensive experiments over two public datasets show the superiority over existing baselines and state-of-the-art sequence-based models.

The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method.

Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing.

Face recognition algorithms often have to solve problems such as facial pose, illumination, and expression (PIE). To reduce the impacts, many researchers have been trying to find the best discriminant transformation in eigenspaces, either linear or nonlinear, to obtain better recognition results. Various researchers have also designed novel matching algorithms to reduce the PIE effects. In this study, a nearest feature space embedding (called NFS embedding) algorithm is proposed for face recognition. The distance between a point and the nearest feature line (NFL) or the NFS is embedded in the transformation through the discriminant analysis. Three factors, including class separability, neighborhood structure preservation, and NFS measurement, were considered to find the most effective and discriminating transformation in eigenspaces. The proposed method was evaluated by several benchmark databases and compared with several state-of-the-art algorithms. According to the compared results, the proposed method outperformed the other algorithms.