Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The d facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Our final combination outperforms the state-of-the-art without employing fine-tuning or ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin

Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (|BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept

Subtypes of Acute Ischemic Stroke

Background: To determine the frequency ofvarious subtypes of acute ischemic stroke amongpatients using the TOAST criteria.Methods: In this prospective, cross sectional study156 consecutive stroke patients fulfilling theinclusion criteria were recruited. Information on riskfactors like age, gender, diabetes and hypertensionwas collected. Physical and neurologicalexamination was done and relevant investigationswere reviewed, to classify the subtype of strokeaccording to TOAST criteria. . Risk factors like age,gender, diabetes and hypertension were comparedwith stroke subtypes after stratification using thechi-square test with significance at p < 0.05.Results: Out of the 156 patients with acute ischemicstroke, mean age at presentation was 63.51 years.Among them 75% had hypertension and 48.1% werediabetics. The various subtypes of acute ischemicstroke were Large artery atherosclerosis(35.3%)whichwas the commonest cause. Large arteryatherosclerosis was found to be more common infemales (47.1% vs 25.6%) whereas cardioembolicstrokes were more common in males (29.1% vs17.1%) (p value 0.02). When hypertension anddiabetes was compared with stroke subtypes theresults were statistically insignificant (p value.>0.05).Conclusion: Higher incidence of large artery andcardioembolic disease was found. Preventive effortsagainst the burden of ischemic stroke should focuson risk factor intervention for each patient accordingto subtype rather than ischemic stroke as a whole

Relasphone - Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland ( boreal zone) were in good agreement with reference forest inventory plot data on pine ( R-2 = 0.75, RMSE = 5.33 m(2)/ha), spruce ( R-2 = 0.75, RMSE = 6.73 m(2)/ha) and birch ( R-2 = 0.71, RMSE = 4.98 m(2)/ha), with total relative RMSE ( %) = 29.66%. In Durango, Mexico ( temperate zone), Relasphone stem volume measurements were best for pine ( R-2 = 0.88, RMSE = 32.46 m(3)/ha) and total stem volume ( R-2 = 0.87, RMSE = 35.21 m(3)/ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.Peer reviewe

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compared to established conventional machine learning approaches. Studied use-cases include mapping of forest disturbance (windthrown forests, snowload damages) using deep change vector analysis, forest height mapping using UNet+ based models, Momentum contrast and regression modeling. Study areas were represented by several boreal forest sites in Finland. Our results indicate that developed methods allow to achieve superior classification and prediction accuracies compared to traditional methodologies and mimimize the amount of necessary in-situ forestry data