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Image segmentation is a basic task of Object-Based Image Analysis (OBIA) in remote sensing. Traditionally, the algorithms for this task are mostly based on region merging procedure. But the segmented objects may correlate poorly with the actual object boundary. Inspired by the recent remarkable improvements on edge detection with deep learning, we propose a semantic edge-guided segmentation with deep learning method to extract meaningful geographic objects from High-resolution Remote Sensing (HRS) images. The method consists of three stages: in the first stage, geographic object boundaries are manually labelled and randomly augmented to generate training data; in the second stage, a fully convolutional neural networks with Encoder-Decoder structure and multiscale supervised nets are trained to detect edges at multiple scales. The detected edges with semantic information do not only include local details but also global edge structure, which are more in accordance with human perception and suitable for conversion to actual geographic boundaries; in the third stage, the detected edges are thinned and extended to construct complete object boundaries according to calculated edge strength. The average precision of our method for the two datasets was 0.902 and 0.866, which is higher than the state-of-the-art deep learning models including RCF, BDCN and DexiNed have obtained. And line IoU improvement of at least 8.46% and F1 score improvement of at least 8.13% in the two datasets. The code of DDLNet is publicly available at . [ABSTRACT FROM AUTHOR]

The basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information. [ABSTRACT FROM AUTHOR]

Mountains are an important kind of landform on the earth's surface. Due to harsh mountainous environment, such as steep slopes and cliffs, remote sensing has become an indispensable tool for surveying mountain areas instead of traditional ground surveys. However, the accuracy of current land cover products derived from remote sensing in mountain areas is still low. In this paper, we propose a three-level architecture for land cover classification in mountain areas. Topographic partitioning is first performed in order to partition a large area into several smaller zones, and then, multiresolution segmentation is implemented in each individual zone. Thus, we can obtain initial geo-semantic objects with terrain, spectrum and texture homogeneities. A fully convolutional network (FCN)-based classifier (U-Net) is further introduced for supervised classification of land cover. From the perspectives of both visual interpretation and quantitative evaluation, the proposed method achieved robust and high-precision results for all land cover types. We also investigate the contributions of multimodal features for classification accuracy improvement. First, the results showed that additional features resulted in higher classification accuracies than 3-features only; 6-features achieved the best performance on farmland, impervious surfaces and coniferous forests, while 5-features performed well on water and broad-leaved forests. The elevation feature did not have a positive effect on water and broad-leaved forests, which can be explained by their physical distribution in the landscape. Second, the most significant improvement was achieved on water (Kappa coefficient increased from 0.741 to 0.924), followed by coniferous forests (Kappa coefficient increased from 0.629 to 0.805), whereas only a minor improvement was observed for the other three types. Furthermore, the accuracies of farmland and impervious surfaces remained relatively high even without the assistance of additional features, and texture feature plays a key role. The final land cover map was generated by combining the optimal results of each type via a hierarchical integrating strategy. The overall accuracy of classification achieved 90.6%. [ABSTRACT FROM AUTHOR]

The Cyanobacteria was catalytically pyrolyzed in methanol atmosphere using magnesium-aluminum layered double oxide/ZSM-5 composites (MgAl-LDO/ZSM-5) as a catalyst. The largest liquid yield of 67.68% was obtained in methanol atmosphere, which was much higher than that obtained without methanol under same reaction condition. The Gas chromatography-Mass spectrometer data proved that the organics phase contained less O-compounds and N-compounds and more aromatic compounds. And the ultimate analysis results showed that this bio-oil had much higher HHV values and lower O/C molar ratio. The co-processing methanol atmosphere and bifunctional catalyst would be a promising means for improving the quality of bio-oil. [ABSTRACT FROM AUTHOR]