LiDAR sensörünün hiperspektral verilerden gölgelik alan çıkarımı başarımına etkisi

Year 2018, Volume: 24 Issue: 2, 198 - 204, 30.04.2018

Seniha Esen Yüksel , Mustafa Boyacı

Abstract

Hiperspektral
görüntülerin analiziyle, tek bir pikselden o pikseldeki materyalin ne olduğu
anlaşılabilmektedir. Bu özelliğiyle hiperspektral görüntüleme, yeryüzünün
uzaktan algılanmasını gerektiren jeoloji, zirai ve askeri alanlarda, özellikle
sınıflandırma ve hedef tespiti uygulamalarında tercih edilen bir yöntem
olmaktadır. Ancak, hiperspektral görüntülemede gölgelik alanlarda kalan
hedeflerden çok az miktarda foton yansımakta, bu yüzden de toplanan spektral
verilerin genlikleri çok düşük düzeylerde kalmaktadır. Bu durum, gölgede kalan
hedefin bulunamamasına neden olabilmektedir. Özellikle yüksekliğin sık
değiştiği yerleşim yerlerinden alınan verilerde, gölgelik alanların etkisinin
sınıflandırma başarımına etkisi katlanarak artmaktadır. Bu çalışmada, gölge tespiti
yapan iki algoritma geliştirilmiş ve karşılaştırılmıştır. İlk yöntemde,
hiperspektral verilere ek olarak LiDAR sensöründen alınan veriler de
kullanılmıştır. LiDAR verilerinden gölge tespiti amacıyla, hiperspektral
verilerin toplanma anındaki güneşin açılarını ve ortamdaki yükseltileri dikkate
alan bir görüş hattı algoritması geliştirilmiştir. İkinci yöntemde ise,
gölgeler sadece hiperspektral veriler kullanılarak tespit edilmiştir. Öncelikle
bir referans imza oluşturulmuş, sonra diğer pikseller bu referans imzaya olan
uzaklıklarına göre sınıflandırılmıştır. Sonuçta, hiperspektral görüntüdeki
gölge alanlar tespit edilmiş ve iki yöntemin eşleşme sonuçları ve
güvenilirlikleri karşılaştırılmıştır.

Keywords

LiDAR, Hiperspektral, Gölgelik alanlar

Effect of LiDAR sensor on the success of shadow detection from hyperspectral data

Abstract

With
the analysis of hyperspectral images, it is possible to understand the
underlying material from a single pixel. Due to this characteristics,
hyperspectral imaging (HSI) is becoming a preferred method in geology,
agriculture and defense fields which require the remote sensing of the
environment for the purposes of classification and target detection. However,
in HSI images, there are only a few photons that get reflected from areas that
are under shadow. Hence, the amplitudes of the spectral signals received from
shadow areas are very small, which leads to tremendous difficulties in target
detection in shadowy areas. These difficulties become much more pronounceable
in areas with varying elevations. In this study, we developed two methods to
find the shadow regions in hyperspectral images and compared their results. The
first method, line-of-sight, uses an external sensor, the Light Detection and
Ranging (LiDAR), which provides elevation information. We use the LiDAR data
and detect the shadows at the time of the hyperspectral data collection. Then
we match the shadows to the hyperspectral image using UTM coordinates. The second
method uses only the hyperspectral data and compares each pixel to a
pre-determined shadow signature to arrive at a shadow/non-shadow decision.
Comparison of both methods gives insight into the reliability of both methods
and allows to better deal with the shadows in hyperspectral data.

Keywords

LiDAR, Hyperspectral, Shadowy areas, Line of sight

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