Rice crop calendar based on phenology analysis from time-series images

Soontranon, Narut; Srestasathiern, Panu; Rakwatin, Preesan

doi:10.1109/ecticon.2015.7207029

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Over the past 20 years, remote sensing imagery has found application in a various sector, encompassing areas such as land-use and land-cover. Furthermore, because of its extensive coverage, satellite data may be used to calculate estimations of agricultural acreage [1]. Over half of the world's population resides in Asia, which is also the main rice-producing region.…”

Section: Literature Reviewmentioning

confidence: 99%

FPGA-Based Satellite Vision Systems using Verilog HDL

Jain

2024

IJRASET

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It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions. There are several methods for extracting these traits. Using a Grey Level Cooccurrence Matrix is the most used method. It is employed to unveil specific characteristics regarding the spatial arrangement of gray levels in the texture image. The Grey Level Co-occurrence Matrix (GLCM) captures statistical details of neighboring pixels in an image, enabling the computation of textural features that enhance the comprehension of visual content. This research presents a VLSI implementation aimed at extracting four texture characteristics from the grey level co-occurrence matrix. Verilog was employed to model the hardware, with MATLAB used for software simulation. The simulation utilized the Verilog HDL language from the XILINX tool, and the implementation was executed on the SPARTAN FPGA board.

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Section: Literature Reviewmentioning

confidence: 99%

FPGA-Based Satellite Vision Systems using Verilog HDL

Jain

2024

IJRASET

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“…Remote sensing imaging has been applied in many application fields such as land-use and land-cover in the last two decades. Moreover satellite imaging is able to compute estimates of agricultural areas due to being able to cover a wide region (Soontranon et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Region of Interest Detection Based on Histogram Segmentation for Satellite Image

Kiadtikornthaweeyot¹,

Tatnall²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:High resolution satellite imaging is considered as the outstanding applicant to extract the Earth's surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.

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Rice crop calendar based on phenology analysis from time-series images

Cited by 2 publications

References 13 publications

FPGA-Based Satellite Vision Systems using Verilog HDL

FPGA-Based Satellite Vision Systems using Verilog HDL

Region of Interest Detection Based on Histogram Segmentation for Satellite Image

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