A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique

Efficient Nonlinear Dimensionality Reduction for Pixel-wise Classification of Hyperspectral Imagery

Classification, target detection, and compression are all important tasks in analyzing hyperspectral imagery (HSI). Because of the high dimensionality of HSI, it is often useful to identify low-dimensional representations of HSI data that can be used to make analysis tasks tractable. Traditional linear dimensionality reduction (DR) methods are not adequate due to the nonlinear distribution of HSI data. Many nonlinear DR methods, which are successful in the general data processing domain, such as Local Linear Embedding (LLE) [1], Isometric Feature Mapping (ISOMAP) [2] and Kernel Principal Components Analysis (KPCA) [3], run very slowly and require large amounts of memory when applied to HSI. For example, applying KPCA to the 512×217 pixel, 204-band Salinas image using a modern desktop computer (AMD FX-6300 Six-Core Processor, 32 GB memory) requires more than 5 days of computing time and 28GB memory! In this thesis, we propose two different algorithms for significantly improving the computational efficiency of nonlinear DR without adversely affecting the performance of classification task: Simple Linear Iterative Clustering (SLIC) superpixels and semi-supervised deep autoencoder networks (SSDAN). SLIC is a very popular algorithm developed for computing superpixels in RGB images that can easily be extended to HSI. Each superpixel includes hundreds or thousands of pixels based on spatial and spectral similarities and is represented by the mean spectrum and spatial position of all of its component pixels. Since the number of superpixels is much smaller than the number of pixels in the image, they can be used as input for nonlinearDR, which significantly reduces the required computation time and memory versus providing all of the original pixels as input. After nonlinear DR is performed using superpixels as input, an interpolation step can be used to obtain the embedding of each original image pixel in the low dimensional space. To illustrate the power of using superpixels in an HSI classification pipeline,we conduct experiments on three widely used and publicly available hyperspectral images: Indian Pines, Salinas and Pavia. The experimental results for all three images demonstrate that for moderately sized superpixels, the overall accuracy of classification using superpixel-based nonlinear DR matches and sometimes exceeds the overall accuracy of classification using pixel-based nonlinear DR, with a computational speed that is two-three orders of magnitude faster. Even though superpixel-based nonlinear DR shows promise for HSI classification, it does have disadvantages. First, it is costly to perform out-of-sample extensions. Second, it does not generalize to handle other types of data that might not have spatial information. Third, the original input pixels cannot approximately be recovered, as is possible in many DR algorithms.In order to overcome these difficulties, a new autoencoder network - SSDAN is proposed. It is a fully-connected semi-supervised autoencoder network that performs nonlinear DR in a manner that enables class information to be integrated. Features learned from SSDAN will be similar to those computed via traditional nonlinear DR, and features from the same class will be close to each other. Once the network is trained well with training data, test data can be easily mapped to the low dimensional embedding. Any kind of data can be used to train a SSDAN,and the decoder portion of the SSDAN can easily recover the initial input with reasonable loss.Experimental results on pixel-based classification in the Indian Pines, Salinas and Pavia images show that SSDANs can approximate the overall accuracy of nonlinear DR while significantly improving computational efficiency. We also show that transfer learning can be use to finetune features of a trained SSDAN for a new HSI dataset. Finally, experimental results on HSI compression show a trade-off between Overall Accuracy (OA) of extracted features and PeakSignal to Noise Ratio (PSNR) of the reconstructed image

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Learning to Propagate Labels on Graphs: An Iterative Multitask Regression Framework for Semi-supervised Hyperspectral Dimensionality Reduction

Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension-reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform