A PRACTICAL METHOD FOR RICE ACREAGE STIMATION WTTH REMOTE SENSING

The crop area estimaton is one of the main fields in application of remotesensing. The paper focuses on the operational method for rice planting areaestimation, in which TM datu is used to ertract base rice area in a given year of1992. The NOAA AVHRR data is used to prwhct the changing tendency of the nceplanting area. The base area data needs to be updated for every rice growth penodupon the availability of TM data. Three methods can be used to extract the base riceplanting area. They are (1) visual interpretation with interaedve adjustmant on thescreen, (2) iflteraCtive automatic classification with manual elinunating of the non-rice pixels on the screen, and (3) automatic dassification with GIS spatial analysis.These methods can be combined to increase reliability and accuracy. The currentpaper is only concemed with the description of the second method. MultitemporalNOAA AVHRR SAVI data are combined as multiband image and are classifiedusing supetwsed makimum likelihood classifier on ERDAS to prediCt the changingtendency of rice planting area. The method has been successfully used in extraCtingearly nce area in Hubei Province in 1994 and acceptable result was obtained.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction:GF-FloodNet

Deep learning algorithms show good prospects for remote sensingflood monitoring.They mostly rely on huge amounts of labeled data.However,there is a lack of available labeled data in actual needs.In this paper,we propose a high-resolution multi-source remote sensing dataset forflood area extraction:GF-FloodNet.GF-FloodNet contains 13388 samples from Gaofen-3(GF-3)and Gaofen-2(GF-2)images.We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it.Compare with otherflood-related datasets,GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels,but also consists of multi-source remote sensing data.We thoroughly validate and evaluate the dataset using several deep learning models,including quantitative analysis,qualitative analysis,and validation on large-scale remote sensing data in real scenes.Experimental results reveal that GF-FloodNet has significant advantages by multi-source data.It can support different deep learning models for training to extractflood areas.There should be a potential optimal boundary for model training in any deep learning dataset.The boundary seems close to 4824 samples in GF-FloodNet.We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

Enhancing the accuracy of area extraction in machine vision-based pig weighing through edge detection

The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig area was examined using the images of 47 pigs of different weights.Relationship between the threshold value and the extracted area was numerically analyzed for both methods.It was found that the accuracy of the conventional method depended heavily on the threshold value,while choice of threshold value in the edge detection approach had no influence on the extracted area over a wide range.In normal lighting conditions,both methods yielded comparable values of predicted weight;however,under variable light intensities,the edge detection method was superior to the conventional method,because the former was proven to be independent of light intensities.This makes edge detection an ideal method for area extraction during the walk-through weighing process where pigs are allowed to move around.

An improved shape shifting method of critical area extraction

As die size and complexity increase, accurate and efficient extraction of the critical area is essential for yield prediction. Aiming at eliminating the potential integration errors of the traditional shape shifting method, an improved shape shifting method is proposed for Manhattan layouts. By mathematical analyses of the relevance of critical areas to defect sizes, the critical area for all defect sizes is modeled as a piecewise quadratic polynomial function of defect size, which can be obtained by extracting critical area for some certain defect sizes. Because the improved method calculates critical areas for all defect sizes instead of several discrete values with traditional shape shifting method, it eliminates the integration error of the average critical area. Experiments on industrial layouts show that the improved shape shifting method can improve the accuracy of the average critical area calculation by 24.3% or reduce about 59.7% computational expense compared with the traditional method.

Classification method of cultivated land based on UAV visible light remote sensing

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.