Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed

Deep Learning Approach for Building Detection Using LiDAR-Orthophoto Fusion

© 2018 Faten Hamed Nahhas et al. This paper reports on a building detection approach based on deep learning (DL) using the fusion of Light Detection and Ranging (LiDAR) data and orthophotos. The proposed method utilized object-based analysis to create objects, a feature-level fusion, an autoencoder-based dimensionality reduction to transform low-level features into compressed features, and a convolutional neural network (CNN) to transform compressed features into high-level features, which were used to classify objects into buildings and background. The proposed architecture was optimized for the grid search method, and its sensitivity to hyperparameters was analyzed and discussed. The proposed model was evaluated on two datasets selected from an urban area with different building types. Results show that the dimensionality reduction by the autoencoder approach from 21 features to 10 features can improve detection accuracy from 86.06% to 86.19% in the working area and from 77.92% to 78.26% in the testing area. The sensitivity analysis also shows that the selection of the hyperparameter values of the model significantly affects detection accuracy. The best hyperparameters of the model are 128 filters in the CNN model, the Adamax optimizer, 10 units in the fully connected layer of the CNN model, a batch size of 8, and a dropout of 0.2. These hyperparameters are critical to improving the generalization capacity of the model. Furthermore, comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area. However, the SVM model achieves better accuracy in the testing area than the proposed model without dimensionality reduction. This study generally shows that the use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR-orthophoto data

A neural system for deforestation monitoring on Landsat images of the Amazon Region

AbstractWe deal with the problem of automating the interpretation of satellite images of the Amazon region for deforestation monitoring. Our approach is based on a combination of image segmentation and classification techniques, the latter emplying a neural-network architecture that works on a fuzzy model of classification. The architecture implements a relaxation mechanism on top of a feedforward neural network, in order to take advantage of the interrelations among neighboring image segments. Our fuzzy, segment-based approach has numerous advantages over more traditional, pixel-based approaches employing statistical techniques. These advantages range from the possibility of treating transition and interference phenomena in the images to the ease with which complex information related to a region's geometry, texture, and contextual setting can be used. We report on a great variety of experiments on representative portions of the Amazon region, employing neural networks trained by the back-propagation algorithm. The results indicate very good overall performance, and allow us to draw some conclusions regarding the effectiveness of the various sources of information available as input to the system. In particular, it appears that simple spectral information, together with textural information on a region's entropy and correlation and simple contextual information, are effective in the classification for deforestation monitoring. It also appears that the effective incorporation of geometric information would require further investigation on possible enhancements to the system

Colour Contrast Occurrence matrix: a vector and perceptual texture feature

International audienceTexture discrimination was the second more important task studied after colour perception and characterization.Nevertheless, few works explore the colour extension of these works and none for vectorial processing ofthis important visual information. In this work we propose a novel and vector processing for colour texturecharacterization, the color contrast occurrence matrix C2O. This new texture feature is based on the colourdierence assessment. To be link to the human perception, the colour dierence is expressed using a perceptualdistance expressed in CIELab and two angles characterizing the chromaticity and darker or lighter direction.Through this new attribute, we analyze the stability to changes in illumination, viewpoint and spectrum of thelight source in front of dierent texture image databases . Thanks to our construction, we avoid the main limit ofexisting texture features requiring an initial colour quantization or a binarization inside the texture construction.Keeping the small local contrast, we obtain a more accurate texture feature description explaining the obtainedresults. Then we carry out the construction of a features vector by occurrence quantization, keeping the initialideas of Julesz, Haralick and Ojala, for the classication purposes. The results show best correct classicationpercentages in databases that with important spatio-chromatic complexity as ALOT

Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages

CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning

In this work, by using descriptive techniques, the characteristics of the texture of the CT (computed tomography) image of patients with colorectal cancer were extracted and, subsequently, classified in KRAS+ or KRAS-. This was accomplished by using different classifiers, such as Support Vector Machine (SVM), Grading Boosting Machine (GBM), Neural Networks (NNET), and Random Forest (RF). Texture analysis can provide a quantitative assessment of tumour heterogeneity by analysing both the distribution and relationship between the pixels in the image. The objective of this research is to demonstrate that CT-based Radiomics can predict the presence of mutation in the KRAS gene in colorectal cancer. This is a retrospective study, with 47 patients from the University Hospital, with a confirmatory pathological analysis of KRAS mutation. The highest accuracy and kappa achieved were 83% and 64.7%, respectively, with a sensitivity of 88.9% and a specificity of 75.0%, achieved by the NNET classifier using the texture feature vectors combining wavelet transform and Haralick coefficients. The fact of being able to identify the genetic expression of a tumour without having to perform either a biopsy or a genetic test is a great advantage, because it prevents invasive procedures that involve complications and may present biases in the sample. As well, it leads towards a more personalized and effective treatmentThis work has received financial support from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2020–2023) and the European Union (European Regional Development Fund—ERDF), Project MTM2016-76969-PS

Integration of Spectral and Textural Features from Ikonos Image to Classify Vegetation Cover in Mountainous Area

Studi ini mengevaluasi penggunaan fitur spektral dan tekstur secara terintegrasi yang didapat dari citra IKONOS untuk mengindentifikasi tipe-tipe tutupan lahan pertanian di daerahpegunungan. Studi meliputi pra pengolahan citra, pengembangan metode kuantisasi citra, penghitungan nilai tekstur, pembuatan dataset dan penilaian akurasi. Pra pengolahan citra berfokus pada registrasi citra dan normalisasi topografis. Dalam studi ini dikembangkan dua metodekuantisasi citra yaitu segmentasi citra dan filter rata-rata. Segmentasi citra mengklasifikasi citra kedalam beberapa segmentasi berdasarkan determinasi jumlah total piksel setiap kelas, sedangkan filter rata-rata mengelompokkan citra berdasarkan rata-rata nilai angka dijital dalam ukuranwindow tertentu. Empat ukuran tekstur yaitu inverse difference moment, contrast, entropy dan energy dihitung dengan grey level co-occurrence matrix (GLCM). Hasil studi menunjukkankombinasi aspek spektral dan tekstur meningkatkan akurasi klasifikasi secara signifikan dibandingkan klasifikasi hanya menggunakan fitur spektral saja. Segmentasi citra dan filter rata-rata dapat memberikan bentuk-bentuk spasial tipe tutupan lahan pertanian yang lebih efektif dibanding menggunakan citra dengan derajat keabuan 256. Ketelitian keseluruhan meningkat 11,33% ketika menggunakan integrasi spektral dan fitur tekstur inverse difference moment (5x5) danenergy (9x9)