Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network

Herbicide use is rising globally to enhance food production,causing harm to environment and the ecosystem.Precision agriculture suggests variable-rate herbicide application based on weed densities to mitigate adverse effects of herbicides.Accurate weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture data.Labelling large agriculture data at pixel level is a time-consuming and tedious job.In this paper,a methodology is developed to accelerate manual labelling of pixels using a two-step procedure.In the first step,the background and foreground are segmented using maximum likelihood classification,and in the second step,the weed pixels are manually labelled.Such labelled data is used to train semantic segmentation models,which classify crop and background pixels as one class,and all other vegetation as the second class.This paper evaluates the proposed methodology on high-resolution colour images of canola fields and makes performance comparison of deep learning meta-architectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50.ResNet-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869.

Parallelizing maximum likelihood classification (MLC) for supervised image classification by pipelined thread approach through high-level synthesis (HLS) on FPGA cluster

High spectral,spatial,vertical and temporal resolution data are increasingly available and result in the serious challenge to pro-cess big remote-sensing images effectively and efficiently.This article introduced how to conduct supervised image classification by implementing maximum likelihood classification(MLC)over big image data on a field programmable gate array(FPGA)cloud.By comparing our prior work of implementing MLC on conventional cluster of multicore computers and graphics processing unit,it can be concluded that FPGAs can achieve the best performance in comparison to conventional CPU cluster and K40 GPU,and are more energy efficient.The proposed pipelined thread approach can be extended to other image-processing solutions to handle big data in the future.

Parallelizing maximum likelihood classification on computer cluster and graphics processing unit for supervised image classification

Supervised image classification has been widely utilized in a variety of remote sensing applications.When large volume of satellite imagery data and aerial photos are increasingly available,high-performance image processing solutions are required to handle large scale of data.This paper introduces how maximum likelihood classification approach is parallelized for implementation on a computer cluster and a graphics processing unit to achieve high performance when processing big imagery data.The solution is scalable and satisfies the need of change detection,object identification,and exploratory analysis on large-scale high-resolution imagery data in remote sensing applications.

Processing of Landsat 8 Imagery and Ground Gamma-Ray Spectrometry for Geologic Mapping and Dose-Rate Assessment, Wadi Diit along the Red Sea Coast, Egypt

Maximum Likelihood (MLH) supervised classification of atmospherically corrected Landsat 8 imagery was applied successfully for delineating main geologic units with a good accuracy (about 90%) according to reliable ground truth areas, which reflected the ability of remote sensing data in mapping poorly-accessed and remote regions such as playa (Sabkha) environs, subdued topography and sand dunes. Ground gamma-ray spectrometric survey was to delineate radioactive anomalies within Quaternary sediments at Wadi Diit. The mean absorbed dose rate (D), annual effective dose equivalent (AEDE) and external hazard index (Hex) were found to be within the average worldwide ranges. Therefore, Wadi Diit environment is said to be radiological hazard safe except at the black-sand lens whose absorbed dose rate of 100.77 nGy/h exceeds the world average. So, the inhabitants will receive a relatively high radioactive dose generated mainly by monazite and zircon minerals from black-sand lens.

1990-2030年艾哈迈达巴德城市增长的评价与预测

Analyzing long term urban growth trends can provide valuable insights into a city’s future growth.This study employs LANDSAT satellite images from 1990,2000,2010 and 2019 to perform a spatiotemporal assessment and predict Ahmedabad’s urban growth.Land Use Land Change(LULC)maps developed using the Maximum Likelihood classifier produce four principal classes:Built-up,Vegetation,Water body,and“Others”.In between 1990-2019,the total built-up area expanded by 130%,132 km^(2) in 1990 to 305 km^(2) in 2019.Rapid population growth is the chief contributor towards urban growth as the city added 3.9 km^(2) of additional built-up area to accommodate every 100,000 new residents.Further,a Multi-Layer Perceptron-Markov Chain model(MLP-MC)predicts Ahmedabad’s urban expansion by 2030.Compared to 2019,the MLP-MC model predicts a 25%and 19%increase in Ahmedabad’s total urban area and population by 2030.Unaltered,these trends shall generate many socio-economic and environmental problems.Thus,future urban development policies must balance further development and environmental damage.

Efficient utilization of multi-core processors and many-core co-processors on supercomputer beacon for scalable geocomputation and geo-simulation over big earth data

Digital earth science data originated from sensors aboard satellites and platforms such as airplane,UAV,and mobile systems are increasingly available with high spectral,spatial,vertical,and temporal resolution data.When such big earth science data are processed and analyzed via geocomputation solutions,or utilized in geospatial simulation or modeling,considerable computing power and resources are necessary to complete the tasks.While classic computer clusters equipped by central processing units(CPUs)and the new computing resources of graphics processing units(GPUs)have been deployed in handling big earth data,coprocessors based on the Intel’s Many Integrated Core(MIC)Architecture are emerging and adopted in many high-performance computer clusters.This paper introduces how to efficiently utilize Intel’s Xeon Phi multicore processors and MIC coprocessors for scalable geocomputation and geo-simulation by implementing two algorithms,Maximum Likelihood Classification(MLC)and Cellular Automata(CA),on supercomputer Beacon,a cluster of MICs.Four different programming models are examined,including(1)the native model,(2)the offload model,(3)the symmetric model,and(4)the hybrid-offload model.It can be concluded that while different kinds of parallel programming models can enable big data handling efficiently,the hybrid-offload model can achieve the best performance and scalability.These different programming models can be applied and extended to other types of geocomputation to handle big earth data.