Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

Face presentation attacks, also known as spoofing attacks, pose a significant threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To prevent spoofing, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we reformulate the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. The proposed frame skipping is based on a uniform sampling approach where the original video is divided into fixed size video clips. In this way, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of each RNNs, a meta-model is developed to improve the overall recognition performance by combining the predictions of the individual RNNs. Extensive experiments were conducted on four datasets, and state-of-the-art performance is reported for MSU-MFSD (3.12\%), Replay-Attack (11.19\%), and OULU-NPU (12.23\%) using half total error rate (HTER) in the most challenging cross-dataset test scenario

Saliency-based Video Summarization for Face Anti-spoofing

Due to the growing availability of face anti-spoofing databases, researchers are increasingly focusing on video-based methods that use hundreds to thousands of images to assess their impact on performance. However, there is no clear consensus on the exact number of frames in a video required to improve the performance of face anti-spoofing tasks. Inspired by the visual saliency theory, we present a video summarization method for face anti-spoofing tasks that aims to enhance the performance and efficiency of deep learning models by leveraging visual saliency. In particular, saliency information is extracted from the differences between the Laplacian and Wiener filter outputs of the source images, enabling identification of the most visually salient regions within each frame. Subsequently, the source images are decomposed into base and detail layers, enhancing representation of important information. The weighting maps are then computed based on the saliency information, indicating the importance of each pixel in the image. By linearly combining the base and detail layers using the weighting maps, the method fuses the source images to create a single representative image that summarizes the entire video. The key contribution of our proposed method lies in demonstrating how visual saliency can be used as a data-centric approach to improve the performance and efficiency of face presentation attack detection models. By focusing on the most salient images or regions within the images, a more representative and diverse training set can be created, potentially leading to more effective models. To validate the method's effectiveness, a simple deep learning architecture (CNN-RNN) was used, and the experimental results showcased state-of-the-art performance on five challenging face anti-spoofing datasets

Patch-Based Discriminative Learning for Remote Sensing Scene Classification

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods

Future Impact of Land Use/Land Cover Changes on Ecosystem Services in the Lower Meghna River Estuary, Bangladesh

Assessing the effects of different land use scenarios on subsequent changes in ecosystem service has great implications for sustainable land management. Here, we designed four land use/land cover (LULC) scenarios, such as business-as-usual development (BAUD), economic development priority (EDP), ecological protection priority (EPP), and afforestation development priority (ADP), through a Cellular Automata-Markov (CA-Markov) model, and their effects on ecosystem service values (ESVs) were predicted, using historical LULC maps and ESV coefficients of the Lower Meghna River Estuary, Bangladesh. Findings revealed that agricultural and mangrove forest lands experienced the greatest decreases, while rural and urban settlement land had the greatest increases, leading to a total ESV decrease of US$105.34 million during 1988-2018. The scenario analysis indicated that ESV in 2038 would also decrease by US$41.37 million and US$16.38 million under the BAUD and EDP scenarios, respectively, while ESV will increase by US$60.61 million and US$130.95 million under the EPP and ADP scenarios, respectively. However, all the future land use scenarios will lead to 1.65%, 10.21%, 7.58%, and 6.75% gaps in total food requirements, respectively. Hence, from the perspective of maximizing ESVs and minimizing the trade-offs in food gaps, the ADP scenario could be the optimal land management policy for the studied landscape

Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh

The adverse impacts of climate change exert mounting pressure on agriculture-dependent livelihoods of many developing and developed nations. However, integrated and spatially specific vulnerability assessments in less-developed countries like Bangladesh are rare, and insufficient to support the decision-making needed for climate-change resilience. Here, we develop an agricultural livelihood vulnerability index (ALVI) and an integrated approach, allowing for (i) mapping out the hot spots of vulnerability distribution; (ii) identifying key factors of spatially heterogeneous vulnerability; and (iii) supporting intervention planning for adaptation. This study conceptualized vulnerability as a function of exposure, sensitivity, and adaptive capacity by developing a composite index from a reliable dataset of 64 indicators comprising biophysical, agro-ecological, and socioeconomic variables. The empirical studies of coastal Bangladesh revealed that Bhola, Patuakhali, and Lakshmipur districts, around the mouth of the deltaic Meghna estuaries, are the hot spot of vulnerability distribution. Furthermore, the spatially heterogeneous vulnerability was triggered by spatial variation of erosion, cyclones, drought, rain-fed agriculture, land degradation, soil phosphorus, crop productivity, sanitation and housing condition, infant mortality, emergency shelters, adoption of agro-technology. The integrated approach could be useful for monitoring and evaluating the effectiveness of adaptation intervention by substituting various hypothetical scenarios into the ALVI framework for baseline comparison

Patch-based discriminative learning for remote sensing scene classification

Abstract The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to explore the spatial dependencies between different image regions and introduce patch-based discriminative learning (PBDL) for remote sensing scene classification. In particular, the proposed method employs multi-level feature learning based on small, medium, and large neighborhood regions to enhance the discriminative power of image representation. To achieve this, image patches are selected through a fixed-size sliding window, and sampling redundancy, a novel concept, is developed to minimize the occurrence of redundant features while sustaining the relevant features for the model. Apart from multi-level learning, we explicitly impose image pyramids to magnify the visual information of the scene images and optimize their positions and scale parameters locally. Motivated by this, a local descriptor is exploited to extract multi-level and multi-scale features that we represent in terms of a codeword histogram by performing k-means clustering. Finally, a simple fusion strategy is proposed to balance the contribution of individual features where the fused features are incorporated into a bidirectional long short-term memory (BiLSTM) network. Experimental results on the NWPU-RESISC45, AID, UC-Merced, and WHU-RS datasets demonstrate that the proposed approach yields significantly higher classification performance in comparison with existing state-of-the-art deep-learning-based methods

Dynamics of Tree outside Forest Land Cover Development and Ecosystem Carbon Storage Change in Eastern Coastal Zone, Bangladesh

Tree outside forest (TOF) has immense potential in economic and environmental development by increasing the amount of tree vegetation in and around rural settlements. It is an important source of carbon stocks and a critical option for climate change regulation, especially in land-scarce, densely populated developing countries such as Bangladesh. Spatio-temporal changes of TOF in the eastern coastal zone of Bangladesh were analyzed and mapped over 1988–2018, using Landsat land use land cover (LULC) maps and associated ecosystem carbon storage change by linking the InVEST carbon model. Landsat TM and OLI-TIRS data were classified through the Maximum Likelihood Classifier (MLC) algorithm using Semi-Automated Classification (SAC). In the InVEST model, aboveground, belowground, dead organic matter, and soil carbon densities of different LULC types were used. The findings revealed that the studied landscapes have differential features and changing trends in LULC where TOF, mangrove forest, built-up land, and salt-aquaculture land have increased due to the loss of agricultural land, mudflats, water bodies, and hill vegetation. Among different land biomes, TOF experienced the largest increase (1453.9 km2), and it also increased carbon storage by 9.01 Tg C. However, agricultural land and hill vegetation decreased rapidly by 1285.8 km2 and 365.7 km2 and reduced carbon storage by 3.09 Tg C and 4.89 Tg C, respectively. The total regional carbon storage increased by 1.27 Tg C during 1988–2018. In addition to anthropogenic drivers, land erosion and accretion were observed to significantly alter LULC and regional carbon storage, necessitating effective river channel and coastal embankment management to minimize food and environmental security tradeoff in the studied landscape

Dynamics of Tree outside Forest Land Cover Development and Ecosystem Carbon Storage Change in Eastern Coastal Zone, Bangladesh

Tree outside forest (TOF) has immense potential in economic and environmental development by increasing the amount of tree vegetation in and around rural settlements. It is an important source of carbon stocks and a critical option for climate change regulation, especially in land-scarce, densely populated developing countries such as Bangladesh. Spatio-temporal changes of TOF in the eastern coastal zone of Bangladesh were analyzed and mapped over 1988–2018, using Landsat land use land cover (LULC) maps and associated ecosystem carbon storage change by linking the InVEST carbon model. Landsat TM and OLI-TIRS data were classified through the Maximum Likelihood Classifier (MLC) algorithm using Semi-Automated Classification (SAC). In the InVEST model, aboveground, belowground, dead organic matter, and soil carbon densities of different LULC types were used. The findings revealed that the studied landscapes have differential features and changing trends in LULC where TOF, mangrove forest, built-up land, and salt-aquaculture land have increased due to the loss of agricultural land, mudflats, water bodies, and hill vegetation. Among different land biomes, TOF experienced the largest increase (1453.9 km2), and it also increased carbon storage by 9.01 Tg C. However, agricultural land and hill vegetation decreased rapidly by 1285.8 km2 and 365.7 km2 and reduced carbon storage by 3.09 Tg C and 4.89 Tg C, respectively. The total regional carbon storage increased by 1.27 Tg C during 1988–2018. In addition to anthropogenic drivers, land erosion and accretion were observed to significantly alter LULC and regional carbon storage, necessitating effective river channel and coastal embankment management to minimize food and environmental security tradeoff in the studied landscape

SAM:self-augmentation mechanism for COVID-19 detection using chest X-ray images

Abstract COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots