Ensemble of ERDTs for Spectral–Spatial Classification of Hyperspectral Images Using MRS Object-Guided Morphological Profiles

In spectral-spatial classification of hyperspectral image tasks, the performance of conventional morphological profiles (MPs) that use a sequence of structural elements (SEs) with predefined sizes and shapes could be limited by mismatching all the sizes and shapes of real-world objects in an image. To overcome such limitation, this paper proposes the use of object-guided morphological profiles (OMPs) by adopting multiresolution segmentation (MRS)-based objects as SEs for morphological closing and opening by geodesic reconstruction. Additionally, the ExtraTrees, bagging, adaptive boosting (AdaBoost), and MultiBoost ensemble versions of the extremely randomized decision trees (ERDTs) are introduced and comparatively investigated for spectral-spatial classification of hyperspectral images. Two hyperspectral benchmark images are used to validate the proposed approaches in terms of classification accuracy. The experimental results confirm the effectiveness of the proposed spatial feature extractors and ensemble classifiers

Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries

Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90

Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles

To investigate the performance of extreme gradient boosting (XGBoost) in remote sensing image classification tasks, XGBoost was first introduced and comparatively investigated for the spectral-spatial classification of hyperspectral imagery using the extended maximally stable extreme-region-guided morphological profiles (EMSER_MPs) proposed in this study. To overcome the potential issues of XGBoost, meta-XGBoost was proposed as an ensemble XGBoost method with classification and regression tree (CART), dropout-introduced multiple additive regression tree (DART), elastic net regression and parallel coordinate descent-based linear regression (linear) and random forest (RaF) boosters. Moreover, to evaluate the performance of the introduced XGBoost approach with different boosters, meta-XGBoost and EMSER_MPs, well-known and widely accepted classifiers, including support vector machine (SVM), bagging, adaptive boosting (AdaBoost), multi class AdaBoost (MultiBoost), extremely randomized decision trees (ExtraTrees), RaF, classification via random forest regression (CVRFR) and ensemble of nested dichotomies with extremely randomized decision tree (END-ERDT) methods, were considered in terms of the classification accuracy and computational efficiency. The experimental results based on two benchmark hyperspectral data sets confirm the superior performance of EMSER_MPs and EMSER_MPs with mean pixel values within region (EMSER_MPsM) compared to that for morphological profiles (MPs), morphological profile with partial reconstruction (MPPR), extended MPs (EMPs), extended MPPR (EMPPR), maximally stable extreme-region-guided morphological profiles (MSER_MPs) and MSER_MPs with mean pixel values within region (MSER_MPsM) features. The proposed meta-XGBoost algorithm is capable of obtaining better results than XGBoost with the CART, DART, linear and RaF boosters, and it could be an alternative to the other considered classifiers in terms of the classification of hyperspectral images using advanced spectral-spatial features, especially from generalized classification accuracy and model training efficiency perspectives

Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30

Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy

Fermented soybean meal improves the growth performance, nutrient digestibility, and microbial flora in piglets

In order to increase nutritive values of soybean meal (SBM), 3 species of microbes were used to ferment SBM. Through a 3 × 3 orthogonal design and parameter measurements of soybean peptide and anti-nutritional factor contents in the fermented soybean meal (FSBM), it was estimated that the best microbial proportion of Bacillus subtilis, Hansenula anomala and Lactobacillus casei was 2:1:2 for SBM fermentation (P  0.05). However, newly-weaned piglets (d 28–38) fed 10% FSBM and different levels of plasma protein obtained higher average daily gain (ADG) and feed conversion ratio (FCR), compared with those without FSBM but with 6% plasma protein (P < 0.05). Piglets (d 38–68) fed diets supplemented with FSBM and soybean protein concentrate (SBPC) at 3.75% and 7.5% respectively increased nutrient digestibility, fecal enzyme activity and lactic acid bacteria counts, and decreased fecal Escherichia coli counts (P < 0.05), compared with the control. These data indicated that FSBM had positive effects on nutrient digestibility and fecal microflora for piglets

Protease gene shuffling and expression in Pichia pastoris

Four kinds of neutral and alkaline protease genes from Aspergillus oryzae and Bacillus subtilis were isolated and shuffled. The shuffled genes were selected, inserted into pGAPZαA plasmid and transformed into Escherichia coli. The gene which could express high-activity protease was selected by screening the sizes of transparent zones around the colonies on casein plates. After an ideal protease gene was selected, it was sequenced and then transformed into Pichia pastoris X33. The result showed that the base in 1022th position of shuffled protease gene was changed from thymine to cytosine, inferring that cysteine was changed to arginine in the mutant protease. After 48 h incubation for the transformed P. pastoris with the mutant or native protease genes, the mutant protease activity was 36.4% higher than the native protease (P<0.05). The optimal pH and temperature of the mutant protease were 6.5-8.0 and 30-70°C, respectively, which indicated better stability than the native protease (P<0.05)

A Hybrid Attention-Aware Fusion Network (HAFNet) for Building Extraction from High-Resolution Imagery and LiDAR Data

Automated extraction of buildings from earth observation (EO) data has long been a fundamental but challenging research topic. Combining data from different modalities (e.g., high-resolution imagery (HRI) and light detection and ranging (LiDAR) data) has shown great potential in building extraction. Recent studies have examined the role that deep learning (DL) could play in both multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may encounter the following limitations: (1) the individual modal and cross-modal features, which we consider both useful and important for final prediction, cannot be sufficiently learned and utilized and (2) the multimodal features are fused by a simple summation or concatenation, which appears ambiguous in selecting cross-modal complementary information. In this paper, we address these two limitations by proposing a hybrid attention-aware fusion network (HAFNet) for building extraction. It consists of RGB-specific, digital surface model (DSM)-specific, and cross-modal streams to sufficiently learn and utilize both individual modal and cross-modal features. Furthermore, an attention-aware multimodal fusion block (Att-MFBlock) was introduced to overcome the fusion problem by adaptively selecting and combining complementary features from each modality. Extensive experiments conducted on two publicly available datasets demonstrated the effectiveness of the proposed HAFNet for building extraction

Mapping Blue and Red Color-Coated Steel Sheet Roof Buildings over China Using Sentinel-2A/B MSIL2A Images

Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, remote sensing (RS) provides abundant data to identify and map CCSS materials with different colors in urban areas. However, existing studies are still quite challenging with regards to the data collection and processing costs, particularly in wide geographical areas. Combining free access high-resolution RS data and a cloud computing platform, i.e., Sentinel-2A/B data sets and Google Earth Engine (GEE), this study aims at CCSS material identification and mapping. Specifically, six novel spectral indexes that use Sentinel-2A/B MSIL2A data are proposed for blue and red CCSS material identification, namely the normalized difference blue building index (NDBBI), the normalized difference red building index NDRBI, the enhanced blue building index (EBBI), the enhanced red building index (ERBI), the logical blue building index (LBBI) and the logical red building index (LRBI). These indexes are qualitatively and quantitatively evaluated on a very large number of urban sites all over the P.R. China and compared with the state-of-the-art redness and blueness indexes (RI and BI, respectively). The results demonstrate that the proposed indexes, specifically the LRBI and LBBI, are highly effective in visual evaluation, clearly detecting and discriminating blue and red CCSS covers from other urban materials. Results show that urban areas from the northern parts of P.R. China have larger proportions of blue and red CCSS materials, and areas of blue and red CCSS material buildings are positively correlated with population and urban size at the provincial level across China

Mapping Blue and Red Color-Coated Steel Sheet Roof Buildings over China Using Sentinel-2A/B MSIL2A Images

Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, remote sensing (RS) provides abundant data to identify and map CCSS materials with different colors in urban areas. However, existing studies are still quite challenging with regards to the data collection and processing costs, particularly in wide geographical areas. Combining free access high-resolution RS data and a cloud computing platform, i.e., Sentinel-2A/B data sets and Google Earth Engine (GEE), this study aims at CCSS material identification and mapping. Specifically, six novel spectral indexes that use Sentinel-2A/B MSIL2A data are proposed for blue and red CCSS material identification, namely the normalized difference blue building index (NDBBI), the normalized difference red building index NDRBI, the enhanced blue building index (EBBI), the enhanced red building index (ERBI), the logical blue building index (LBBI) and the logical red building index (LRBI). These indexes are qualitatively and quantitatively evaluated on a very large number of urban sites all over the P.R. China and compared with the state-of-the-art redness and blueness indexes (RI and BI, respectively). The results demonstrate that the proposed indexes, specifically the LRBI and LBBI, are highly effective in visual evaluation, clearly detecting and discriminating blue and red CCSS covers from other urban materials. Results show that urban areas from the northern parts of P.R. China have larger proportions of blue and red CCSS materials, and areas of blue and red CCSS material buildings are positively correlated with population and urban size at the provincial level across China