Detailed and accurate information on the spatial variation of land cover use is a critical component local ecology environmental research. For these tasks, high resolution images are required. Considering trade-off between temporal in remote sensing images, many learning-based models (e.g., Convolutional neural network, sparse coding, Bayesian network) have been established to improve coarse both computer vision fields. However, data for training testing methods usually limited certain...

The thermal band of a satellite platform enables the measurement land surface temperature (LST), which captures spatial-temporal distribution energy exchange between Earth and atmosphere. LST plays critical role in simulation models, enhancing our understanding physical biochemical processes nature. However, limitations swath width orbit altitude prevent single sensor from providing data with both high spatial temporal resolution. To tackle this challenge, unmixing-based spatiotemporal...

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their spectral resolution, spatial and signal-to-noise ratio (SNR). However, the tradeoffs between three core parameters imager (SNR, resolution) should be considered designing an efficient imaging system. Only few reported studies on analysis impact SNR available. Further, mutual interactions among these rarely considered. In this empirical study, our aim was to understand...

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability environmental carrying capacity. This research proposed method to integrate time series Sentinel-1 (S1) Sentinel-2 (S2) data over oasis agricultural areas through case study in Northwest China. Previous studies using synthetic aperture radar (SAR) alone often yield quite limited accuracy identification due speckles. To improve quality SAR features, we adopted statistically homogeneous...

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due tree crop rotation and spectral characteristics in red-edge region is presented. It can be adopted produce maps high spatial resolution (30 m) at large scales, with use open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate Imaging Spectroradiometer (MODIS), Sentinel-2 MultiSpectral Instrument (MSI), as well a free cloud computing...

In emergency responses to natural disasters, actionable information provided by remote sensing images is crucial help managers become aware of the situation and assess magnitude damage. Without accurate prediction time consumption, choosing an algorithm for land use/land cover (LULC) classification under these circumstances could be blind subjective. Here, we proposed a full parameter complexity (FPTC) analysis corresponding coefficient ω estimate actual running LULC without actually code....

Accurate and efficient extraction of cultivated land data is great significance for agricultural resource monitoring national food security. Deep-learning-based classification remote-sensing images overcomes the two difficulties traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF)) when extracting land: (1) limited performance same land-cover type with high intra-class spectral variation, such as both vegetation non-vegetation cover,...

Hyperspectral remote sensing image classification has been widely employed for numerous applications, such as environmental monitoring, agriculture, and mineralogy. During classification, the number of training samples in each class often varies significantly. This imbalance dataset is not identified because most classifiers are designed under a balanced assumption, which can distort minority classes or even treat them noise. may lead to biased inaccurate results. issue be alleviated by...

The unmixing-based spatiotemporal fusion model is one of the effective ways to solve limitations in temporal and spatial resolution tradeoffs a single satellite sensor. By using data from different platforms, high both domains can be produced. However, due ill-posed characteristic unmixing function, performance may vary setups. key factors affecting stability most how set up strategy for downscaling remain unknown. In this study, we use multisource land surface temperature as case focus on...

Stripe noise still affects full-spectrum airborne hyperspectral imager (FAHI) images after laboratory radiometric calibration, which seriously the subsequent applications of imager. Therefore, two state-of-the-art methods, median linear correction (MLC) and Fourier transform filtering (FTF), were proposed to restore FAHI images, residual stripes removed in most cases. However, these methods have their own limitations. For instance, restored image has a slight "shadow" cases where...

The microstructure and its relationship with the mechanical properties of an Inconel 718 superalloy fabricated by selective laser melting (SLM) technology were investigated under different post processes, including HT1, HT2, HT2+HIP as-built conditions. Specifically, HT1 involved solution treatment at 960 °C for 1 h followed air cooling (AC) aging 720 8 furnace (FC) to 620 h. HT2 980 AC 760 FC 650 included same treatments as additional Hot Isostatic Pressing (HIP) 1170 4 100 MPa....

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, stronger immunity, more serious loss detailed information, which easily leads to neglect effective characteristics. In view difficulty balancing immunity and characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed image segmentation. distance between pixels seed points in established by...

Tropospheric correction is a crucial step for interferometric synthetic aperture radar (InSAR) monitoring of small deformation magnitude. However, most the corrections are implemented without rigorous evaluation their influences on InSAR measurements. In this paper, we present three statistical metrics to evaluate performance. Firstly, propose time series decomposition method estimate tropospheric noise and mitigate bias caused by ground displacement. On basis, calculate root-mean-square...

In this work, hierarchically porous MgCo2O4 nanochain networks were successfully synthesized by a novel template-free method realized via facile solvothermal synthesis followed heat treatment. The morphologies of precursor could be adjusted from nanosheets to nanobelts and finally interwoven nanowires, depending on the volume ratio diethylene glycol deionized water in solution. After calcination, nanowires transformed hierarchical with marco-/meso-porosity, which are composed 10–20 nm...

The deep-learning-network performance depends on the accuracy of training samples. samples are commonly labeled by human visual investigation or inherited from historical land-cover land-use maps, which usually contain label noise, depending subjective knowledge and time map. Helping network to distinguish noisy labels during process is a prerequisite for applying model across locations. This study proposes an antinoise framework, Weight Loss Network (WLN), achieve this goal. WLN contains...

Deep-learning-based object detectors have substantially improved state-of-the-art detection in remote sensing images terms of precision and degree automation. Nevertheless, the large variation scales makes it difficult to achieve high-quality across multiresolution images, where quality is defined by Intersection over Union (IoU) threshold used training. In addition, imbalance between positive negative samples worsens precision. Recently, was found that a Cascade region-based convolutional...

Detailed and accurate information on the spatial variation of soil over low-relief areas is a critical component environmental studies agricultural management. Early show that pattern dynamics provides comprehensive about can be used as new covariate to indicate in low relief areas. In practice, however, data gaps caused by cloud cover lead incomplete patterns large area. Missing reduce accuracy make it hard compare two from different locations. this study, we introduced method fill based...

Abstract. Short-term precipitation commonly occurs in south part of China, which brings intensive local region for very short time. Massive water would cause the flood inside city when amount beyond capacity drainage system. Thousands people’s life could be influenced by those short-term disasters and higher managements are required to facing these challenges. How predict occurrence heavy accurately is one worthwhile scientific questions meteorology. According recent studies, accuracy...

Aiming at the problem of misdetection caused by traditional texture characteristic extraction model, which does not describe correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, divided into multi-scale objects graph-based superpixel segmentation, and optimal scale determined overall goodness F-measure (OGF). Then, except for spectral features, multi-channel features (CCM) are extracted to...