IGARSS 2011.ppt

Archaeological Land Use Characterization using Multispectral Remote Sensing Data Dr. Iván Esteban Villalón Turrubiates, Member, IEEE María de Jesús Llovera Torres Monitoring Hidrological Variations using Multispectral SPOT-5 Data: Regional Case of Jalisco in Mexico Dr. Iván Esteban Villalón Turrubiates, Member, IEEE UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES

Overview Abstract Remote Sensing Definition Sensor Resolution Introduction to Image Classification Model Formalism Verification Protocols Simulation Experiments Concluding Remarks

Abstract Proposition - A new and efficient classification approach of remote sensing signatures extracted from large-scale multispectral imagery. Contribution - This approach exploits the idea of combining the spectral signatures from a remote sensing image to perform a novel and accurate classification technique. Verification - Simulation results are provided to verify the efficiency of the proposed approach.

Remote Sensing Remote Sensing can be defined as: "The arte and science to obtain data from an object avoiding direct contact with it” (Jensen 2000). There is a transmission medium involved?

Remote Sensing Of the Environment : … is the collection of information regarding our Planet surface and its phenomena involving sensors that are not in direct contact with the studied area. The main focus is in recollected information from a spatial perspective throughout electromagnetic radiation transmission.

Remote Sensing Sensor election . Reception, storage and digital signal processing of the data . Analysis of the resulting information.

A) Illumination Source B) Radiation C) Interaction with the object D) Radiation sensing E) Transmission, reception and data processing F) Analysis and interpretation G) Application Process

Resolution All remote sensing systems use four types of resolution: Spatial Spectral Temporal Radiometric

Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 8 August 3

Radiometric Resolution 6-bits Range 0 63 8-bits Range 0 255 0 10-bits Range 1023

INTRODUCTION TO IMAGE CLASSIFICATION

Image Classification Why classify? Make sense of a landscape Place landscape into categories (classes) Forest, Agriculture, Water, Soil, etc. Classification scheme = structure of classes Depends on needs of users.

Typical uses Provide context Landscape planning or assessment Research projects Natural resources management Archaeological studies Drive models Meteorology Biodiversity Water distribution Land use

Example: Near Mary’s Peak Derived from a 1988 Landsat TM image Distinguish types of forest Open Semi-open Broadleaf Mixed Young Conifer Mature Conifer Old Conifer Legend

Classification: Critical Point LAND COVER not necessarily equivalent to LAND USE We focus on what’s there: LAND COVER Many users are interested in how what is there is being used: LAND USE Example Grass is land cover; pasture and recreational parks are land uses of grass

Basic Strategy: How to do it? Use radiometric properties of remote sensor Different objects have different spectral signatures

In an easy world, all “vegetation” pixels would have exactly the same spectral signature. Then we could just say that any pixel in an image with that signature was vegetation. We could do the same for soil, water, etc. to end up with a map of classes. Basic Strategy: How to do it?

Basic Strategy: How to do it? But in reality, that is not the case. Looking at several pixels with vegetation, you’d see variety in spectral signatures. The same would happen for other types of pixels, as well.

The Classification Trick: Deal with variability Different ways of dealing with the variability lead to different ways of classifying images. To talk about this, we need to look at spectral signatures a little differently.

Think of a pixel’s brightness in a 2-Dimensional space. The pixel occupies a point in that space. The vegetation pixel and the soil pixel occupy different points in a 2-D space.

With variability, the vegetation pixels now occupy a region, not a point, of n-Dimensional space. Soil pixels occupy a different region of n-Dimensional space.

Basic Strategy: Deal with variability Classification: Delineate boundaries of classes in n-dimensional space Assign class names to pixels using those boundaries

Classification Strategies Two basic strategies: Supervised Classification We impose our perceptions on the spectral data. Unsupervised Classification Spectral data imposes constraints on our interpretation.

Supervised Classification The computer then creates... Supervised classification requires the analyst to select training areas where he knows what is on the ground and then digitize a polygon within that area… Mean Spectral Signatures Known Conifer Area Known Water Area Known Deciduous Area Digital Image Conifer Deciduous Water

Supervised Classification Multispectral Image Information (Classified Image) Mean Spectral Signatures Spectral Signature of Next Pixel to be Classified Conifer Deciduous Water Unknown

The Result: Image Signatures Water Conifer Deciduous Legend: Land Cover Map

Unsupervised Classification In unsupervised classification, the spectral data imposes constraints on our interpretation. How? Rather than defining training sets and carving out pieces of n-Dimensional space, we define no classes beforehand and instead use statistical approaches to divide the n-Dimensional space into clusters with the best separation. After the fact, we assign class names to those clusters.

Unsupervised Classification Digital Image The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4

Unsupervised Classification Output Classified Image Saved Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4 Unknown Next Pixel to be Classified

Unsupervised Classification It is a simple process to regroup (recode) the clusters into meaningful information classes (the legend). The result is essentially the same as that of the supervised classification: Conif. Hardw. Water Land Cover Map Legend Water Water Conifer Conifer Hardwood Hardwood Labels

Multispectral Imaging Is a technology originally developed for space-based imaging. Multispectral images are the main type of images acquired by remote sensing radiometers. Usually, remote sensing systems have from 3 to 7 radiometers; each one acquires one digital image in a small band of visible spectra, ranging 450 to 690 nm, called red-green-blue (RGB) regions: Blue -> 450-520 nm. Green -> 520-600 nm. Red -> 600-690 nm. The combination of the RGB spectral bands generates the so-called True-Color RS images.

Statistical Approach. Assume normal distributions of pixels within classes. For each class, build a discriminant function For each pixel in the image, this function calculates the probability that the pixel is a member of that class. Takes into account mean and variance of training set. Each pixel is assigned to the class for which it has the highest probability of membership. Weighted Pixel Statistics Method

Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures… Relative Reflectance Weighted Pixel Statistics Method

Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 The candidate pixel clearly belongs to the signature 2 group. Relative Reflectance Weighted Pixel Statistics Method

Weighted Pixel Statistics Method

Verification Protocols A set of three synthesized images are used as verification protocols. All synthesized images are True-Color (RGB), presented in 1024-by-1024 pixels (TIFF format). Each synthesized image contains three different regions (in yellow, blue and black colors) with a different pattern. The developed Weighted Pixel Statistics ( WPS ) algorithm is compared with the most traditional Weighted Order Statistics ( WOS ) method [S.W. Perry, H.S. Wong, 2002].

Results: 1 st Synthesized Scene Synthesized Scene WOS Classification WPS Classification

Quantitative Comparison 1 st Synthesized Scene

Results: 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

Qualitative Comparison 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

Quantitative Comparison 2 nd Synthesized Scene

Results: 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

Qualitative Comparison 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

Quantitative Comparison 3 rd Synthesized Scene

Remarks The quantitative study is performed calculating the classified percentage obtained with the WOS and WPS methods, respectively. The WOS method uses only 1 spectral band. The WPS method uses the information from the three spectral bands to analyze the pixel-level neighborhood means and variances. The results shows a more accurate and less smoothed identification of the classes.

Archaeological Land Use A Remote Sensing Signatures (RSS) electronic map is extracted from the multispectral image. Three level RSS are selected for this particular simulation process, defined as: ██ – Archaeological land use zones. ██ – Modern land use zones. ██ – Natural land cover zones. ██ – Unclassified zones.

Archaeological Site "Guachimontones", Jalisco Mexico

Simulation Results Scene from "Guachimontones" Original Scene WPS Classification

Hidrological Variations A Remote Sensing Signatures (RSS) electronic map is extracted from the multispectral image. Three level RSS are selected for this particular simulation process, defined as: ██ – Humid zones. ██ – Dry zones. ██ – Wet zones. ██ – Unclassified zones.

Simulation Results Scene from "La Vega" dam, Jalisco Mexico Original Scene WPS Classification

Remarks The WOS classifier generates several unclassified zones because it uses only one spectral band in the classification process. The WPS classifier provides a high-accurate classification without unclassified zones because it uses more robust information in the processing. The qualitative and quantitative analysis probe the efficiency of the proposed approach.

Future Work Comparison with several classification techniques. A more extensive performance analysis of the proposed approach with different synthesized images. Application to remote sensing imagery and the study of its performance. Hardware implementation of the proposed approach.

Dr. Iván Esteban Villalón Turrubiates, Member, IEEE UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES THANK YOU! Questions?

IGARSS 2011.ppt

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